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| <title>Kaggle Titanic Tutorial ยท Hivemall User Manual</title> |
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| <ul class="summary"> |
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| <li> |
| <a href="https://hivemall.incubator.apache.org/" target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> |
| </li> |
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| <li class="header">TABLE OF CONTENTS</li> |
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| <li class="chapter " data-level="1.1" data-path="../"> |
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| <a href="../"> |
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| <b>1.1.</b> |
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| Introduction |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.2" data-path="../getting_started/"> |
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| <a href="../getting_started/"> |
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| <b>1.2.</b> |
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| Getting Started |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="1.2.1" data-path="../getting_started/installation.html"> |
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| <a href="../getting_started/installation.html"> |
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| <b>1.2.1.</b> |
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| Installation |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.2.2" data-path="../getting_started/permanent-functions.html"> |
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| <a href="../getting_started/permanent-functions.html"> |
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| <b>1.2.2.</b> |
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| Install as permanent functions |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.2.3" data-path="../getting_started/input-format.html"> |
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| <a href="../getting_started/input-format.html"> |
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| <b>1.2.3.</b> |
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| Input Format |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="1.3" data-path="../misc/funcs.html"> |
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| <a href="../misc/funcs.html"> |
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| <b>1.3.</b> |
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| List of Functions |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4" data-path="../tips/"> |
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| <a href="../tips/"> |
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| <b>1.4.</b> |
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| Tips for Effective Hivemall |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="1.4.1" data-path="../tips/addbias.html"> |
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| <a href="../tips/addbias.html"> |
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| <b>1.4.1.</b> |
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| Explicit add_bias() for better prediction |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4.2" data-path="../tips/rand_amplify.html"> |
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| <a href="../tips/rand_amplify.html"> |
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| <b>1.4.2.</b> |
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| Use rand_amplify() to better prediction results |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4.3" data-path="../tips/rt_prediction.html"> |
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| <a href="../tips/rt_prediction.html"> |
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| <b>1.4.3.</b> |
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| Real-time prediction on RDBMS |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4.4" data-path="../tips/ensemble_learning.html"> |
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| <a href="../tips/ensemble_learning.html"> |
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| <b>1.4.4.</b> |
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| Ensemble learning for stable prediction |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4.5" data-path="../tips/mixserver.html"> |
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| <a href="../tips/mixserver.html"> |
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| <b>1.4.5.</b> |
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| Mixing models for a better prediction convergence (MIX server) |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.4.6" data-path="../tips/emr.html"> |
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| <a href="../tips/emr.html"> |
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| <b>1.4.6.</b> |
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| Run Hivemall on Amazon Elastic MapReduce |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="1.5" data-path="../tips/general_tips.html"> |
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| <a href="../tips/general_tips.html"> |
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| <b>1.5.</b> |
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| General Hive/Hadoop Tips |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="1.5.1" data-path="../tips/rowid.html"> |
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| <a href="../tips/rowid.html"> |
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| <b>1.5.1.</b> |
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| Adding rowid for each row |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.5.2" data-path="../tips/hadoop_tuning.html"> |
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| <a href="../tips/hadoop_tuning.html"> |
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| <b>1.5.2.</b> |
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| Hadoop tuning for Hivemall |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="1.6" data-path="../troubleshooting/"> |
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| <a href="../troubleshooting/"> |
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| <b>1.6.</b> |
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| Troubleshooting |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="1.6.1" data-path="../troubleshooting/oom.html"> |
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| <a href="../troubleshooting/oom.html"> |
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| <b>1.6.1.</b> |
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| OutOfMemoryError in training |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.6.2" data-path="../troubleshooting/mapjoin_task_error.html"> |
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| <a href="../troubleshooting/mapjoin_task_error.html"> |
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| <b>1.6.2.</b> |
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| SemanticException generate map join task error: Cannot serialize object |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.6.3" data-path="../troubleshooting/asterisk.html"> |
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| <a href="../troubleshooting/asterisk.html"> |
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| <b>1.6.3.</b> |
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| Asterisk argument for UDTF does not work |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.6.4" data-path="../troubleshooting/num_mappers.html"> |
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| <a href="../troubleshooting/num_mappers.html"> |
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| <b>1.6.4.</b> |
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| The number of mappers is less than input splits in Hadoop 2.x |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="1.6.5" data-path="../troubleshooting/mapjoin_classcastex.html"> |
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| <a href="../troubleshooting/mapjoin_classcastex.html"> |
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| <b>1.6.5.</b> |
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| Map-side join causes ClassCastException on Tez |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="header">Part II - Generic Features</li> |
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| <li class="chapter " data-level="2.1" data-path="../misc/generic_funcs.html"> |
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| <a href="../misc/generic_funcs.html"> |
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| <b>2.1.</b> |
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| List of Generic Hivemall Functions |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="2.2" data-path="../misc/topk.html"> |
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| <a href="../misc/topk.html"> |
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| <b>2.2.</b> |
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| Efficient Top-K Query Processing |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="2.3" data-path="../misc/tokenizer.html"> |
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| <a href="../misc/tokenizer.html"> |
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| <b>2.3.</b> |
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| Text Tokenizer |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="2.4" data-path="../misc/approx.html"> |
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| <a href="../misc/approx.html"> |
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| <b>2.4.</b> |
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| Approximate Aggregate Functions |
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| </a> |
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| </li> |
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| <li class="header">Part III - Feature Engineering</li> |
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| <li class="chapter " data-level="3.1" data-path="../ft_engineering/scaling.html"> |
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| <a href="../ft_engineering/scaling.html"> |
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| <b>3.1.</b> |
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| Feature Scaling |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.2" data-path="../ft_engineering/hashing.html"> |
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| <a href="../ft_engineering/hashing.html"> |
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| <b>3.2.</b> |
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| Feature Hashing |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.3" data-path="../ft_engineering/selection.html"> |
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| <a href="../ft_engineering/selection.html"> |
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| <b>3.3.</b> |
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| Feature Selection |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.4" data-path="../ft_engineering/binning.html"> |
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| <a href="../ft_engineering/binning.html"> |
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| <b>3.4.</b> |
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| Feature Binning |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.5" data-path="../ft_engineering/pairing.html"> |
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| <a href="../ft_engineering/pairing.html"> |
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| <b>3.5.</b> |
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| Feature Paring |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="3.5.1" data-path="../ft_engineering/polynomial.html"> |
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| <a href="../ft_engineering/polynomial.html"> |
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| <b>3.5.1.</b> |
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| Polynomial features |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="3.6" data-path="../ft_engineering/ft_trans.html"> |
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| <a href="../ft_engineering/ft_trans.html"> |
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| <b>3.6.</b> |
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| Feature Transformation |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="3.6.1" data-path="../ft_engineering/vectorization.html"> |
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| <a href="../ft_engineering/vectorization.html"> |
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| <b>3.6.1.</b> |
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| Feature vectorization |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.6.2" data-path="../ft_engineering/quantify.html"> |
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| <a href="../ft_engineering/quantify.html"> |
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| <b>3.6.2.</b> |
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| Quantify non-number features |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.6.3" data-path="../ft_engineering/binarize.html"> |
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| <a href="../ft_engineering/binarize.html"> |
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| <b>3.6.3.</b> |
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| Binarize label |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.6.4" data-path="../ft_engineering/onehot.html"> |
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| <a href="../ft_engineering/onehot.html"> |
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| <b>3.6.4.</b> |
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| One-hot encoding |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="3.7" data-path="../ft_engineering/term_vector.html"> |
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| <a href="../ft_engineering/term_vector.html"> |
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| <b>3.7.</b> |
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| Term Vector Model |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="3.7.1" data-path="../ft_engineering/tfidf.html"> |
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| <a href="../ft_engineering/tfidf.html"> |
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| <b>3.7.1.</b> |
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| TF-IDF Term Weighting |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="3.7.2" data-path="../ft_engineering/bm25.html"> |
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| <a href="../ft_engineering/bm25.html"> |
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| <b>3.7.2.</b> |
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| Okapi BM25 Term Weighting |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="header">Part IV - Evaluation</li> |
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| <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html"> |
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| <a href="../eval/binary_classification_measures.html"> |
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| <b>4.1.</b> |
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| Binary Classification Metrics |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="4.1.1" data-path="../eval/auc.html"> |
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| <a href="../eval/auc.html"> |
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| <b>4.1.1.</b> |
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| Area under the ROC curve |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html"> |
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| <a href="../eval/multilabel_classification_measures.html"> |
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| <b>4.2.</b> |
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| Multi-label Classification Metrics |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="4.3" data-path="../eval/regression.html"> |
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| <a href="../eval/regression.html"> |
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| <b>4.3.</b> |
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| Regression Metrics |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="4.4" data-path="../eval/rank.html"> |
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| <a href="../eval/rank.html"> |
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| <b>4.4.</b> |
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| Ranking Measures |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="4.5" data-path="../eval/datagen.html"> |
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| <a href="../eval/datagen.html"> |
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| <b>4.5.</b> |
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| Data Generation |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html"> |
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| <a href="../eval/lr_datagen.html"> |
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| <b>4.5.1.</b> |
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| Logistic Regression data generation |
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| </a> |
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| </li> |
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| </ul> |
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| </li> |
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| <li class="header">Part V - Supervised Learning</li> |
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| <li class="chapter " data-level="5.1" data-path="../supervised_learning/prediction.html"> |
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| <a href="../supervised_learning/prediction.html"> |
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| <b>5.1.</b> |
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| How Prediction Works |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="5.2" data-path="../supervised_learning/tutorial.html"> |
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| <a href="../supervised_learning/tutorial.html"> |
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| <b>5.2.</b> |
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| Step-by-Step Tutorial on Supervised Learning |
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| </a> |
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| </li> |
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| <li class="header">Part VI - Binary Classification</li> |
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| <li class="chapter " data-level="6.1" data-path="general.html"> |
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| <a href="general.html"> |
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| <b>6.1.</b> |
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| Binary Classification |
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| </a> |
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| </li> |
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| <li class="chapter " data-level="6.2" data-path="a9a.html"> |
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| <a href="a9a.html"> |
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| <b>6.2.</b> |
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| a9a Tutorial |
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| </a> |
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| <ul class="articles"> |
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| <li class="chapter " data-level="6.2.1" data-path="a9a_dataset.html"> |
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| <a href="a9a_dataset.html"> |
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| <b>6.2.1.</b> |
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| Data Preparation |
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| </a> |
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| |
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| </li> |
| |
| <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html"> |
| |
| <a href="a9a_generic.html"> |
| |
| |
| <b>6.2.2.</b> |
| |
| General Binary Classifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html"> |
| |
| <a href="a9a_lr.html"> |
| |
| |
| <b>6.2.3.</b> |
| |
| Logistic Regression |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html"> |
| |
| <a href="a9a_minibatch.html"> |
| |
| |
| <b>6.2.4.</b> |
| |
| Mini-batch Gradient Descent |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3" data-path="news20.html"> |
| |
| <a href="news20.html"> |
| |
| |
| <b>6.3.</b> |
| |
| News20 Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.3.1" data-path="news20_dataset.html"> |
| |
| <a href="news20_dataset.html"> |
| |
| |
| <b>6.3.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.2" data-path="news20_pa.html"> |
| |
| <a href="news20_pa.html"> |
| |
| |
| <b>6.3.2.</b> |
| |
| Perceptron, Passive Aggressive |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.3" data-path="news20_scw.html"> |
| |
| <a href="news20_scw.html"> |
| |
| |
| <b>6.3.3.</b> |
| |
| CW, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.4" data-path="news20_generic.html"> |
| |
| <a href="news20_generic.html"> |
| |
| |
| <b>6.3.4.</b> |
| |
| General Binary Classifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.5" data-path="news20_generic_bagging.html"> |
| |
| <a href="news20_generic_bagging.html"> |
| |
| |
| <b>6.3.5.</b> |
| |
| Baggnig classiers |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.6" data-path="news20_adagrad.html"> |
| |
| <a href="news20_adagrad.html"> |
| |
| |
| <b>6.3.6.</b> |
| |
| AdaGradRDA, AdaGrad, AdaDelta |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.7" data-path="news20_rf.html"> |
| |
| <a href="news20_rf.html"> |
| |
| |
| <b>6.3.7.</b> |
| |
| Random Forest |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.8" data-path="news20b_xgboost.html"> |
| |
| <a href="news20b_xgboost.html"> |
| |
| |
| <b>6.3.8.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.4" data-path="kdd2010a.html"> |
| |
| <a href="kdd2010a.html"> |
| |
| |
| <b>6.4.</b> |
| |
| KDD2010a Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.4.1" data-path="kdd2010a_dataset.html"> |
| |
| <a href="kdd2010a_dataset.html"> |
| |
| |
| <b>6.4.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.4.2" data-path="kdd2010a_scw.html"> |
| |
| <a href="kdd2010a_scw.html"> |
| |
| |
| <b>6.4.2.</b> |
| |
| PA, CW, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.5" data-path="kdd2010b.html"> |
| |
| <a href="kdd2010b.html"> |
| |
| |
| <b>6.5.</b> |
| |
| KDD2010b Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.5.1" data-path="kdd2010b_dataset.html"> |
| |
| <a href="kdd2010b_dataset.html"> |
| |
| |
| <b>6.5.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.5.2" data-path="kdd2010b_arow.html"> |
| |
| <a href="kdd2010b_arow.html"> |
| |
| |
| <b>6.5.2.</b> |
| |
| AROW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.6" data-path="webspam.html"> |
| |
| <a href="webspam.html"> |
| |
| |
| <b>6.6.</b> |
| |
| Webspam Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.6.1" data-path="webspam_dataset.html"> |
| |
| <a href="webspam_dataset.html"> |
| |
| |
| <b>6.6.1.</b> |
| |
| Data Pareparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.6.2" data-path="webspam_scw.html"> |
| |
| <a href="webspam_scw.html"> |
| |
| |
| <b>6.6.2.</b> |
| |
| PA1, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter active" data-level="6.7" data-path="titanic_rf.html"> |
| |
| <a href="titanic_rf.html"> |
| |
| |
| <b>6.7.</b> |
| |
| Kaggle Titanic Tutorial |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.8" data-path="criteo.html"> |
| |
| <a href="criteo.html"> |
| |
| |
| <b>6.8.</b> |
| |
| Criteo Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.8.1" data-path="criteo_dataset.html"> |
| |
| <a href="criteo_dataset.html"> |
| |
| |
| <b>6.8.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.8.2" data-path="criteo_ffm.html"> |
| |
| <a href="criteo_ffm.html"> |
| |
| |
| <b>6.8.2.</b> |
| |
| Field-Aware Factorization Machines |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part VII - Multiclass Classification</li> |
| |
| |
| |
| <li class="chapter " data-level="7.1" data-path="../multiclass/news20.html"> |
| |
| <a href="../multiclass/news20.html"> |
| |
| |
| <b>7.1.</b> |
| |
| News20 Multiclass Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="7.1.1" data-path="../multiclass/news20_dataset.html"> |
| |
| <a href="../multiclass/news20_dataset.html"> |
| |
| |
| <b>7.1.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.2" data-path="../multiclass/news20_one-vs-the-rest_dataset.html"> |
| |
| <a href="../multiclass/news20_one-vs-the-rest_dataset.html"> |
| |
| |
| <b>7.1.2.</b> |
| |
| Data Preparation for one-vs-the-rest classifiers |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.3" data-path="../multiclass/news20_pa.html"> |
| |
| <a href="../multiclass/news20_pa.html"> |
| |
| |
| <b>7.1.3.</b> |
| |
| PA |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.4" data-path="../multiclass/news20_scw.html"> |
| |
| <a href="../multiclass/news20_scw.html"> |
| |
| |
| <b>7.1.4.</b> |
| |
| CW, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.5" data-path="../multiclass/news20_xgboost.html"> |
| |
| <a href="../multiclass/news20_xgboost.html"> |
| |
| |
| <b>7.1.5.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.6" data-path="../multiclass/news20_ensemble.html"> |
| |
| <a href="../multiclass/news20_ensemble.html"> |
| |
| |
| <b>7.1.6.</b> |
| |
| Ensemble learning |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.7" data-path="../multiclass/news20_one-vs-the-rest.html"> |
| |
| <a href="../multiclass/news20_one-vs-the-rest.html"> |
| |
| |
| <b>7.1.7.</b> |
| |
| one-vs-the-rest Classifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2" data-path="../multiclass/iris.html"> |
| |
| <a href="../multiclass/iris.html"> |
| |
| |
| <b>7.2.</b> |
| |
| Iris Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="7.2.1" data-path="../multiclass/iris_dataset.html"> |
| |
| <a href="../multiclass/iris_dataset.html"> |
| |
| |
| <b>7.2.1.</b> |
| |
| Data preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2.2" data-path="../multiclass/iris_scw.html"> |
| |
| <a href="../multiclass/iris_scw.html"> |
| |
| |
| <b>7.2.2.</b> |
| |
| SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2.3" data-path="../multiclass/iris_randomforest.html"> |
| |
| <a href="../multiclass/iris_randomforest.html"> |
| |
| |
| <b>7.2.3.</b> |
| |
| Random Forest |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2.4" data-path="../multiclass/iris_xgboost.html"> |
| |
| <a href="../multiclass/iris_xgboost.html"> |
| |
| |
| <b>7.2.4.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part VIII - Regression</li> |
| |
| |
| |
| <li class="chapter " data-level="8.1" data-path="../regression/general.html"> |
| |
| <a href="../regression/general.html"> |
| |
| |
| <b>8.1.</b> |
| |
| Regression |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.2" data-path="../regression/e2006.html"> |
| |
| <a href="../regression/e2006.html"> |
| |
| |
| <b>8.2.</b> |
| |
| E2006-tfidf Regression Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="8.2.1" data-path="../regression/e2006_dataset.html"> |
| |
| <a href="../regression/e2006_dataset.html"> |
| |
| |
| <b>8.2.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html"> |
| |
| <a href="../regression/e2006_generic.html"> |
| |
| |
| <b>8.2.2.</b> |
| |
| General Regessor |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html"> |
| |
| <a href="../regression/e2006_arow.html"> |
| |
| |
| <b>8.2.3.</b> |
| |
| Passive Aggressive, AROW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.2.4" data-path="../regression/e2006_xgboost.html"> |
| |
| <a href="../regression/e2006_xgboost.html"> |
| |
| |
| <b>8.2.4.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="8.3" data-path="../regression/kddcup12tr2.html"> |
| |
| <a href="../regression/kddcup12tr2.html"> |
| |
| |
| <b>8.3.</b> |
| |
| KDDCup 2012 Track 2 CTR Prediction Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="8.3.1" data-path="../regression/kddcup12tr2_dataset.html"> |
| |
| <a href="../regression/kddcup12tr2_dataset.html"> |
| |
| |
| <b>8.3.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.3.2" data-path="../regression/kddcup12tr2_lr.html"> |
| |
| <a href="../regression/kddcup12tr2_lr.html"> |
| |
| |
| <b>8.3.2.</b> |
| |
| Logistic Regression, Passive Aggressive |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.3.3" data-path="../regression/kddcup12tr2_lr_amplify.html"> |
| |
| <a href="../regression/kddcup12tr2_lr_amplify.html"> |
| |
| |
| <b>8.3.3.</b> |
| |
| Logistic Regression with amplifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="8.3.4" data-path="../regression/kddcup12tr2_adagrad.html"> |
| |
| <a href="../regression/kddcup12tr2_adagrad.html"> |
| |
| |
| <b>8.3.4.</b> |
| |
| AdaGrad, AdaDelta |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part IX - Recommendation</li> |
| |
| |
| |
| <li class="chapter " data-level="9.1" data-path="../recommend/cf.html"> |
| |
| <a href="../recommend/cf.html"> |
| |
| |
| <b>9.1.</b> |
| |
| Collaborative Filtering |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="9.1.1" data-path="../recommend/item_based_cf.html"> |
| |
| <a href="../recommend/item_based_cf.html"> |
| |
| |
| <b>9.1.1.</b> |
| |
| Item-based Collaborative Filtering |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="9.2" data-path="../recommend/news20.html"> |
| |
| <a href="../recommend/news20.html"> |
| |
| |
| <b>9.2.</b> |
| |
| News20 Related Article Recommendation Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="9.2.1" data-path="../multiclass/news20_dataset.html"> |
| |
| <a href="../multiclass/news20_dataset.html"> |
| |
| |
| <b>9.2.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.2.2" data-path="../recommend/news20_jaccard.html"> |
| |
| <a href="../recommend/news20_jaccard.html"> |
| |
| |
| <b>9.2.2.</b> |
| |
| LSH/MinHash and Jaccard Similarity |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.2.3" data-path="../recommend/news20_knn.html"> |
| |
| <a href="../recommend/news20_knn.html"> |
| |
| |
| <b>9.2.3.</b> |
| |
| LSH/MinHash and Brute-force Search |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.2.4" data-path="../recommend/news20_bbit_minhash.html"> |
| |
| <a href="../recommend/news20_bbit_minhash.html"> |
| |
| |
| <b>9.2.4.</b> |
| |
| kNN search using b-Bits MinHash |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3" data-path="../recommend/movielens.html"> |
| |
| <a href="../recommend/movielens.html"> |
| |
| |
| <b>9.3.</b> |
| |
| MovieLens Movie Recommendation Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="9.3.1" data-path="../recommend/movielens_dataset.html"> |
| |
| <a href="../recommend/movielens_dataset.html"> |
| |
| |
| <b>9.3.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3.2" data-path="../recommend/movielens_cf.html"> |
| |
| <a href="../recommend/movielens_cf.html"> |
| |
| |
| <b>9.3.2.</b> |
| |
| Item-based Collaborative Filtering |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3.3" data-path="../recommend/movielens_mf.html"> |
| |
| <a href="../recommend/movielens_mf.html"> |
| |
| |
| <b>9.3.3.</b> |
| |
| Matrix Factorization |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3.4" data-path="../recommend/movielens_fm.html"> |
| |
| <a href="../recommend/movielens_fm.html"> |
| |
| |
| <b>9.3.4.</b> |
| |
| Factorization Machine |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3.5" data-path="../recommend/movielens_slim.html"> |
| |
| <a href="../recommend/movielens_slim.html"> |
| |
| |
| <b>9.3.5.</b> |
| |
| SLIM for fast top-k Recommendation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="9.3.6" data-path="../recommend/movielens_cv.html"> |
| |
| <a href="../recommend/movielens_cv.html"> |
| |
| |
| <b>9.3.6.</b> |
| |
| 10-fold Cross Validation (Matrix Factorization) |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part X - Anomaly Detection</li> |
| |
| |
| |
| <li class="chapter " data-level="10.1" data-path="../anomaly/lof.html"> |
| |
| <a href="../anomaly/lof.html"> |
| |
| |
| <b>10.1.</b> |
| |
| Outlier Detection using Local Outlier Factor (LOF) |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="10.2" data-path="../anomaly/sst.html"> |
| |
| <a href="../anomaly/sst.html"> |
| |
| |
| <b>10.2.</b> |
| |
| Change-Point Detection using Singular Spectrum Transformation (SST) |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="10.3" data-path="../anomaly/changefinder.html"> |
| |
| <a href="../anomaly/changefinder.html"> |
| |
| |
| <b>10.3.</b> |
| |
| ChangeFinder: Detecting Outlier and Change-Point Simultaneously |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part XI - Clustering</li> |
| |
| |
| |
| <li class="chapter " data-level="11.1" data-path="../clustering/lda.html"> |
| |
| <a href="../clustering/lda.html"> |
| |
| |
| <b>11.1.</b> |
| |
| Latent Dirichlet Allocation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="11.2" data-path="../clustering/plsa.html"> |
| |
| <a href="../clustering/plsa.html"> |
| |
| |
| <b>11.2.</b> |
| |
| Probabilistic Latent Semantic Analysis |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part XII - GeoSpatial Functions</li> |
| |
| |
| |
| <li class="chapter " data-level="12.1" data-path="../geospatial/latlon.html"> |
| |
| <a href="../geospatial/latlon.html"> |
| |
| |
| <b>12.1.</b> |
| |
| Lat/Lon functions |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part XIII - Hivemall on SparkSQL</li> |
| |
| |
| |
| <li class="chapter " data-level="13.1" data-path="../spark/getting_started/README.md"> |
| |
| <span> |
| |
| |
| <b>13.1.</b> |
| |
| Getting Started |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="13.1.1" data-path="../spark/getting_started/installation.html"> |
| |
| <a href="../spark/getting_started/installation.html"> |
| |
| |
| <b>13.1.1.</b> |
| |
| Installation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="13.2" data-path="../spark/binaryclass/"> |
| |
| <a href="../spark/binaryclass/"> |
| |
| |
| <b>13.2.</b> |
| |
| Binary Classification |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="13.2.1" data-path="../spark/binaryclass/a9a_sql.html"> |
| |
| <a href="../spark/binaryclass/a9a_sql.html"> |
| |
| |
| <b>13.2.1.</b> |
| |
| a9a Tutorial for SQL |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="13.3" data-path="../spark/binaryclass/"> |
| |
| <a href="../spark/binaryclass/"> |
| |
| |
| <b>13.3.</b> |
| |
| Regression |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="13.3.1" data-path="../spark/regression/e2006_sql.html"> |
| |
| <a href="../spark/regression/e2006_sql.html"> |
| |
| |
| <b>13.3.1.</b> |
| |
| E2006-tfidf Regression Tutorial for SQL |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part XIV - Hivemall on Docker</li> |
| |
| |
| |
| <li class="chapter " data-level="14.1" data-path="../docker/getting_started.html"> |
| |
| <a href="../docker/getting_started.html"> |
| |
| |
| <b>14.1.</b> |
| |
| Getting Started |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| |
| |
| <li class="header">Part XIV - External References</li> |
| |
| |
| |
| <li class="chapter " data-level="15.1" > |
| |
| <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome"> |
| |
| |
| <b>15.1.</b> |
| |
| Hivemall on Apache Pig |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| |
| |
| <li class="divider"></li> |
| |
| <li> |
| <a href="https://www.gitbook.com" target="blank" class="gitbook-link"> |
| Published with GitBook |
| </a> |
| </li> |
| </ul> |
| |
| |
| </nav> |
| |
| |
| </div> |
| |
| <div class="book-body"> |
| |
| <div class="body-inner"> |
| |
| |
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| <div class="book-header" role="navigation"> |
| |
| |
| <!-- Title --> |
| <h1> |
| <i class="fa fa-circle-o-notch fa-spin"></i> |
| <a href=".." >Kaggle Titanic Tutorial</a> |
| </h1> |
| </div> |
| |
| |
| |
| |
| <div class="page-wrapper" tabindex="-1" role="main"> |
| <div class="page-inner"> |
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| <section class="normal markdown-section"> |
| |
| <!-- |
| Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| distributed with this work for additional information |
| regarding copyright ownership. The ASF licenses this file |
| to you under the Apache License, Version 2.0 (the |
| "License"); you may not use this file except in compliance |
| with the License. You may obtain a copy of the License at |
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| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| KIND, either express or implied. See the License for the |
| specific language governing permissions and limitations |
| under the License. |
| --> |
| <p>This examples gives a basic usage of RandomForest on Hivemall using <a href="https://www.kaggle.com/c/titanic" target="_blank">Kaggle Titanic</a> dataset. |
| The example gives a baseline score without any feature engineering.</p> |
| <!-- toc --><div id="toc" class="toc"> |
| |
| <ul> |
| <li><a href="#data-preparation">Data preparation</a><ul> |
| <li><a href="#data-preparation-for-randomforest">Data preparation for RandomForest</a></li> |
| </ul> |
| </li> |
| <li><a href="#training">Training</a></li> |
| <li><a href="#prediction">Prediction</a></li> |
| <li><a href="#kaggle-submission">Kaggle submission</a></li> |
| <li><a href="#graphviz-export">Graphviz export</a></li> |
| <li><a href="#test-by-dividing-training-dataset">Test by dividing training dataset</a><ul> |
| <li><a href="#tracing-predictions">Tracing predictions</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </div><!-- tocstop --> |
| <h1 id="data-preparation">Data preparation</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">database</span> titanic; |
| <span class="hljs-keyword">use</span> titanic; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> train; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> train ( |
| passengerid <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- unique id</span> |
| survived <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- target label</span> |
| pclass <span class="hljs-built_in">int</span>, |
| <span class="hljs-keyword">name</span> <span class="hljs-keyword">string</span>, |
| sex <span class="hljs-keyword">string</span>, |
| age <span class="hljs-built_in">int</span>, |
| sibsp <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Siblings/Spouses Aboard</span> |
| parch <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Parents/Children Aboard</span> |
| ticket <span class="hljs-keyword">string</span>, |
| fare <span class="hljs-keyword">double</span>, |
| cabin <span class="hljs-keyword">string</span>, |
| embarked <span class="hljs-keyword">string</span> |
| ) |
| <span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> |
| <span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'|'</span> |
| <span class="hljs-keyword">LINES</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'\n'</span> |
| <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/dataset/titanic/train'</span>; |
| </code></pre> |
| <pre><code class="lang-sh">hadoop fs -rm /dataset/titanic/train/train.csv |
| awk <span class="hljs-string">'{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$4=substr($4,2,length($4)-2);print $0}'</span> train.csv | hadoop fs -put - /dataset/titanic/train/train.csv |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> test_raw; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> test_raw ( |
| passengerid <span class="hljs-built_in">int</span>, |
| pclass <span class="hljs-built_in">int</span>, |
| <span class="hljs-keyword">name</span> <span class="hljs-keyword">string</span>, |
| sex <span class="hljs-keyword">string</span>, |
| age <span class="hljs-built_in">int</span>, |
| sibsp <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Siblings/Spouses Aboard</span> |
| parch <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Parents/Children Aboard</span> |
| ticket <span class="hljs-keyword">string</span>, |
| fare <span class="hljs-keyword">double</span>, |
| cabin <span class="hljs-keyword">string</span>, |
| embarked <span class="hljs-keyword">string</span> |
| ) |
| <span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> |
| <span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'|'</span> |
| <span class="hljs-keyword">LINES</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'\n'</span> |
| <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/dataset/titanic/test_raw'</span>; |
| </code></pre> |
| <pre><code class="lang-sh">hadoop fs -rm /dataset/titanic/<span class="hljs-built_in">test</span>_raw/test.csv |
| awk <span class="hljs-string">'{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$3=substr($3,2,length($3)-2);print $0}'</span> test.csv | hadoop fs -put - /dataset/titanic/<span class="hljs-built_in">test</span>_raw/test.csv |
| </code></pre> |
| <h2 id="data-preparation-for-randomforest">Data preparation for RandomForest</h2> |
| <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:output_row=<span class="hljs-literal">true</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> train_rf; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> train_rf |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">WITH</span> train_quantified <span class="hljs-keyword">as</span> ( |
| <span class="hljs-keyword">select</span> |
| quantify( |
| ${output_row}, passengerid, survived, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked |
| ) <span class="hljs-keyword">as</span> (passengerid, survived, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked) |
| <span class="hljs-keyword">from</span> ( |
| <span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> train |
| <span class="hljs-keyword">order</span> <span class="hljs-keyword">by</span> passengerid <span class="hljs-keyword">asc</span> |
| ) t |
| ) |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-keyword">rand</span>(<span class="hljs-number">31</span>) <span class="hljs-keyword">as</span> rnd, |
| passengerid, |
| <span class="hljs-built_in">array</span>(pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked) <span class="hljs-keyword">as</span> features, |
| survived |
| <span class="hljs-keyword">from</span> |
| train_quantified |
| ; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> test_rf; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> test_rf |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">WITH</span> test_quantified <span class="hljs-keyword">as</span> ( |
| <span class="hljs-keyword">select</span> |
| quantify( |
| output_row, passengerid, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked |
| ) <span class="hljs-keyword">as</span> (passengerid, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked) |
| <span class="hljs-keyword">from</span> ( |
| <span class="hljs-comment">-- need training data to assign consistent ids to categorical variables</span> |
| <span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> ( |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-number">1</span> <span class="hljs-keyword">as</span> train_first, <span class="hljs-literal">false</span> <span class="hljs-keyword">as</span> output_row, passengerid, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked |
| <span class="hljs-keyword">from</span> |
| train |
| <span class="hljs-keyword">union</span> all |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-number">2</span> <span class="hljs-keyword">as</span> train_first, <span class="hljs-literal">true</span> <span class="hljs-keyword">as</span> output_row, passengerid, pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked |
| <span class="hljs-keyword">from</span> |
| test_raw |
| ) t0 |
| <span class="hljs-keyword">order</span> <span class="hljs-keyword">by</span> train_first <span class="hljs-keyword">asc</span>, passengerid <span class="hljs-keyword">asc</span> |
| ) t1 |
| ) |
| <span class="hljs-keyword">select</span> |
| passengerid, |
| <span class="hljs-built_in">array</span>(pclass, <span class="hljs-keyword">name</span>, sex, age, sibsp, parch, ticket, fare, cabin, embarked) <span class="hljs-keyword">as</span> features |
| <span class="hljs-keyword">from</span> |
| test_quantified |
| ; |
| </code></pre> |
| <h1 id="training">Training</h1> |
| <p><code>select guess_attribute_types(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked) from train limit 1;</code></p> |
| <blockquote> |
| <p>Q,C,C,Q,Q,Q,C,Q,C,C</p> |
| </blockquote> |
| <p><code>Q</code> and <code>C</code> represent quantitative variable and categorical variables, respectively.</p> |
| <div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p>Note that the output of <code>guess_attribute_types</code> is not perfect. Revise it by your self. |
| For example, <code>pclass</code> is a categorical variable.</p></div></div> |
| <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:attrs=C,C,C,Q,Q,Q,C,Q,C,C; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> model_rf; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> model_rf |
| <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">select</span> |
| train_randomforest_classifier(features, survived, <span class="hljs-string">"-trees 500 -attrs ${attrs}"</span>) |
| <span class="hljs-keyword">from</span> |
| train_rf |
| ; |
| |
| <span class="hljs-keyword">select</span> |
| array_sum(var_importance) <span class="hljs-keyword">as</span> var_importance, |
| <span class="hljs-keyword">sum</span>(oob_errors) / <span class="hljs-keyword">sum</span>(oob_tests) <span class="hljs-keyword">as</span> oob_err_rate |
| <span class="hljs-keyword">from</span> |
| model_rf; |
| </code></pre> |
| <blockquote> |
| <p>[137.00242639169272,1194.2140119834373,328.78017188176966,628.2568660509628,200.31275032394072,160.12876797647078,1083.5987543408116,664.1234312561456,422.89449844090393,130.72019667694784] 0.18742985409652077</p> |
| </blockquote> |
| <h1 id="prediction">Prediction</h1> |
| <pre><code class="lang-sql"><span class="hljs-comment">-- SET hivevar:classification=true;</span> |
| <span class="hljs-keyword">set</span> hive.<span class="hljs-keyword">auto</span>.<span class="hljs-keyword">convert</span>.<span class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>; |
| <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span class="hljs-literal">false</span>; |
| <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span class="hljs-number">16</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted_rf; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted_rf |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">SELECT</span> |
| passengerid, |
| predicted.label, |
| predicted.probability, |
| predicted.probabilities |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| passengerid, |
| rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| t.passengerid, |
| p.model_weight, |
| tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, <span class="hljs-string">"-classification"</span>) <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later</span> |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| model_id, model_weight, <span class="hljs-keyword">model</span> |
| <span class="hljs-keyword">FROM</span> |
| model_rf |
| <span class="hljs-keyword">DISTRIBUTE</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) |
| ) p |
| <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> test_rf t |
| ) t1 |
| <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> |
| passengerid |
| ) t2 |
| ; |
| </code></pre> |
| <div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p><code>tree_predict_v1</code> is for the backward compatibility for using prediction models built before <code>v0.5.0</code> on <code>v0.5.0</code> or later.</p></div></div> |
| <h1 id="kaggle-submission">Kaggle submission</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted_rf_submit; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted_rf_submit |
| <span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> |
| <span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">","</span> |
| <span class="hljs-keyword">LINES</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">"\n"</span> |
| <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">SELECT</span> passengerid, label <span class="hljs-keyword">as</span> survived |
| <span class="hljs-keyword">FROM</span> predicted_rf |
| <span class="hljs-keyword">ORDER</span> <span class="hljs-keyword">BY</span> passengerid <span class="hljs-keyword">ASC</span>; |
| </code></pre> |
| <pre><code class="lang-sh">hadoop fs -getmerge /user/hive/warehouse/titanic.db/predicted_rf_submit predicted_rf_submit.csv |
| sed -i <span class="hljs-_">-e</span> <span class="hljs-string">"1i PassengerId,Survived"</span> predicted_rf_submit.csv |
| </code></pre> |
| <p>Accuracy would gives <code>0.76555</code> for a Kaggle submission.</p> |
| <h1 id="graphviz-export">Graphviz export</h1> |
| <div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p><code>tree_export</code> feature is supported from Hivemall v0.5.0 or later. |
| Better to limit tree depth on training by <code>-depth</code> option to plot a Decision Tree.</p></div></div> |
| <p>Hivemall provide <code>tree_export</code> to export a decision tree into <a href="https://www.graphviz.org/" target="_blank">Graphviz</a> or human-readable Javascript format. You can find the usage by issuing the following query:</p> |
| <pre><code>> select tree_export("","-help"); |
| |
| usage: tree_export(string model, const string options, optional |
| array<string> featureNames=null, optional array<string> |
| classNames=null) - exports a Decision Tree model as javascript/dot] |
| [-help] [-output_name <arg>] [-r] [-t <arg>] |
| -help Show function help |
| -output_name,--outputName <arg> output name [default: predicted] |
| -r,--regression Is regression tree or not |
| -t,--type <arg> Type of output [default: js, |
| javascript/js, graphviz/dot |
| </code></pre><pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> model_exported |
| <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> ORC tblproperties(<span class="hljs-string">"orc.compress"</span>=<span class="hljs-string">"SNAPPY"</span>) |
| <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">select</span> |
| model_id, |
| tree_export(<span class="hljs-keyword">model</span>, <span class="hljs-string">"-type javascript -output_name survived"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'pclass'</span>,<span class="hljs-string">'name'</span>,<span class="hljs-string">'sex'</span>,<span class="hljs-string">'age'</span>,<span class="hljs-string">'sibsp'</span>,<span class="hljs-string">'parch'</span>,<span class="hljs-string">'ticket'</span>,<span class="hljs-string">'fare'</span>,<span class="hljs-string">'cabin'</span>,<span class="hljs-string">'embarked'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'no'</span>,<span class="hljs-string">'yes'</span>)) <span class="hljs-keyword">as</span> js, |
| tree_export(<span class="hljs-keyword">model</span>, <span class="hljs-string">"-type graphviz -output_name survived"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'pclass'</span>,<span class="hljs-string">'name'</span>,<span class="hljs-string">'sex'</span>,<span class="hljs-string">'age'</span>,<span class="hljs-string">'sibsp'</span>,<span class="hljs-string">'parch'</span>,<span class="hljs-string">'ticket'</span>,<span class="hljs-string">'fare'</span>,<span class="hljs-string">'cabin'</span>,<span class="hljs-string">'embarked'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'no'</span>,<span class="hljs-string">'yes'</span>)) <span class="hljs-keyword">as</span> dot |
| <span class="hljs-keyword">from</span> |
| model_rf |
| <span class="hljs-comment">-- limit 1</span> |
| ; |
| </code></pre> |
| <p><a href="https://gist.github.com/myui/a83ba3795bad9b278cf8bcc59f946e2c#file-titanic-dot" target="_blank">Here is an example</a> plotting a decision tree using Graphviz or <a href="https://viz-js.com/" target="_blank">Vis.js</a>.</p> |
| <h1 id="test-by-dividing-training-dataset">Test by dividing training dataset</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> train_rf_07; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> train_rf_07 |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> train_rf |
| <span class="hljs-keyword">where</span> rnd < <span class="hljs-number">0.7</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> test_rf_03; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> test_rf_03 |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> train_rf |
| <span class="hljs-keyword">where</span> rnd >= <span class="hljs-number">0.7</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> model_rf_07; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> model_rf_07 |
| <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">select</span> |
| train_randomforest_classifier(features, survived, <span class="hljs-string">"-trees 500 -attrs ${attrs}"</span>) |
| <span class="hljs-keyword">from</span> |
| train_rf_07; |
| |
| <span class="hljs-keyword">select</span> |
| array_sum(var_importance) <span class="hljs-keyword">as</span> var_importance, |
| <span class="hljs-keyword">sum</span>(oob_errors) / <span class="hljs-keyword">sum</span>(oob_tests) <span class="hljs-keyword">as</span> oob_err_rate |
| <span class="hljs-keyword">from</span> |
| model_rf_07; |
| </code></pre> |
| <blockquote> |
| <p>[116.12055542977338,960.8569891444097,291.08765260103837,469.74671636586226,163.721292772701,120.784769882858,847.9769298113661,554.4617571355476,346.3500941757221,97.42593940113392] 0.1838351822503962</p> |
| </blockquote> |
| <pre><code class="lang-sql"><span class="hljs-comment">-- SET hivevar:classification=true;</span> |
| <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span class="hljs-literal">false</span>; |
| <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span class="hljs-number">16</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted_rf_03; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted_rf_03 |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">SELECT</span> |
| passengerid, |
| predicted.label, |
| predicted.probability, |
| predicted.probabilities |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| passengerid, |
| rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| t.passengerid, |
| p.model_weight, |
| tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, <span class="hljs-string">"-classification"</span>) <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-comment">-- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted</span> |
| <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later</span> |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| model_id, model_weight, <span class="hljs-keyword">model</span> |
| <span class="hljs-keyword">FROM</span> |
| model_rf_07 |
| <span class="hljs-keyword">DISTRIBUTE</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) |
| ) p |
| <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> test_rf_03 t |
| ) t1 |
| <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> |
| passengerid |
| ) t2; |
| </code></pre> |
| <pre><code class="lang-sql">WITH rf_submit_03 as ( |
| <span class="hljs-keyword">select</span> |
| t.survived <span class="hljs-keyword">as</span> actual, |
| p.label <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-keyword">from</span> |
| test_rf_03 t |
| <span class="hljs-keyword">JOIN</span> predicted_rf_03 p <span class="hljs-keyword">on</span> (t.passengerid = p.passengerid) |
| ) |
| <span class="hljs-keyword">select</span> <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">if</span>(actual=predicted,<span class="hljs-number">1</span>,<span class="hljs-number">0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> accuracy |
| <span class="hljs-keyword">from</span> rf_submit_03; |
| </code></pre> |
| <blockquote> |
| <p>0.8153846153846154</p> |
| </blockquote> |
| <h2 id="tracing-predictions">Tracing predictions</h2> |
| <p>Find important attributes and conditions predicted to survive.</p> |
| <pre><code class="lang-sql">WITH tmp as ( |
| <span class="hljs-keyword">SELECT</span> |
| t.survived <span class="hljs-keyword">as</span> actual, |
| decision_path(m.model_id, m.<span class="hljs-keyword">model</span>, t.features, <span class="hljs-string">'-classification -no_verbose'</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'pclass'</span>,<span class="hljs-string">'name'</span>,<span class="hljs-string">'sex'</span>,<span class="hljs-string">'age'</span>,<span class="hljs-string">'sibsp'</span>,<span class="hljs-string">'parch'</span>,<span class="hljs-string">'ticket'</span>,<span class="hljs-string">'fare'</span>,<span class="hljs-string">'cabin'</span>,<span class="hljs-string">'embarked'</span>)) <span class="hljs-keyword">as</span> <span class="hljs-keyword">path</span> |
| <span class="hljs-keyword">FROM</span> |
| model_rf_07 m |
| <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> <span class="hljs-comment">-- CROSS JOIN</span> |
| test_rf_03 t |
| ) |
| <span class="hljs-keyword">select</span> |
| r.branch, |
| <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> cnt |
| <span class="hljs-keyword">from</span> |
| tmp l |
| LATERAL <span class="hljs-keyword">VIEW</span> explode(array_slice(<span class="hljs-keyword">path</span>, <span class="hljs-number">0</span>, <span class="hljs-number">-1</span>)) r <span class="hljs-keyword">as</span> branch |
| <span class="hljs-keyword">where</span> |
| <span class="hljs-comment">-- actual = 1 and -- actual is survived</span> |
| last_element(<span class="hljs-keyword">path</span>) = <span class="hljs-number">1</span> <span class="hljs-comment">-- predicted is survived</span> |
| <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> |
| r.branch |
| <span class="hljs-keyword">order</span> <span class="hljs-keyword">by</span> |
| cnt <span class="hljs-keyword">desc</span> |
| <span class="hljs-keyword">limit</span> <span class="hljs-number">100</span>; |
| </code></pre> |
| <table> |
| <thead> |
| <tr> |
| <th style="text-align:left">r.branch</th> |
| <th style="text-align:left">cnt</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td style="text-align:left">sex != 0.0</td> |
| <td style="text-align:left">29786</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">pclass != 3.0</td> |
| <td style="text-align:left">18520</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">pclass = 3.0</td> |
| <td style="text-align:left">7444</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">sex = 0.0</td> |
| <td style="text-align:left">6494</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">embarked != 1.0</td> |
| <td style="text-align:left">6175</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">ticket != 22.0</td> |
| <td style="text-align:left">5560</td> |
| </tr> |
| <tr> |
| <td style="text-align:left">...</td> |
| <td style="text-align:left">...</td> |
| </tr> |
| </tbody> |
| </table> |
| <p><div id="page-footer" class="localized-footer"><hr><!-- |
| Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| distributed with this work for additional information |
| regarding copyright ownership. The ASF licenses this file |
| to you under the Apache License, Version 2.0 (the |
| "License"); you may not use this file except in compliance |
| with the License. You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, |
| software distributed under the License is distributed on an |
| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| KIND, either express or implied. See the License for the |
| specific language governing permissions and limitations |
| under the License. |
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