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| <ul class="summary"> |
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| <a href="https://hivemall.incubator.apache.org/" target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> |
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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="../binaryclass/general.html"> |
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| <a href="../binaryclass/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="../binaryclass/a9a.html"> |
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| <a href="../binaryclass/a9a.html"> |
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| <b>6.2.</b> |
| |
| 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="../binaryclass/a9a_dataset.html"> |
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| <a href="../binaryclass/a9a_dataset.html"> |
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| <b>6.2.1.</b> |
| |
| Data Preparation |
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| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.2.2" data-path="../binaryclass/a9a_generic.html"> |
| |
| <a href="../binaryclass/a9a_generic.html"> |
| |
| |
| <b>6.2.2.</b> |
| |
| General Binary Classifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.2.3" data-path="../binaryclass/a9a_lr.html"> |
| |
| <a href="../binaryclass/a9a_lr.html"> |
| |
| |
| <b>6.2.3.</b> |
| |
| Logistic Regression |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.2.4" data-path="../binaryclass/a9a_minibatch.html"> |
| |
| <a href="../binaryclass/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="../binaryclass/news20.html"> |
| |
| <a href="../binaryclass/news20.html"> |
| |
| |
| <b>6.3.</b> |
| |
| News20 Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.3.1" data-path="../binaryclass/news20_dataset.html"> |
| |
| <a href="../binaryclass/news20_dataset.html"> |
| |
| |
| <b>6.3.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.2" data-path="../binaryclass/news20_pa.html"> |
| |
| <a href="../binaryclass/news20_pa.html"> |
| |
| |
| <b>6.3.2.</b> |
| |
| Perceptron, Passive Aggressive |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.3" data-path="../binaryclass/news20_scw.html"> |
| |
| <a href="../binaryclass/news20_scw.html"> |
| |
| |
| <b>6.3.3.</b> |
| |
| CW, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.4" data-path="../binaryclass/news20_generic.html"> |
| |
| <a href="../binaryclass/news20_generic.html"> |
| |
| |
| <b>6.3.4.</b> |
| |
| General Binary Classifier |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.5" data-path="../binaryclass/news20_generic_bagging.html"> |
| |
| <a href="../binaryclass/news20_generic_bagging.html"> |
| |
| |
| <b>6.3.5.</b> |
| |
| Baggnig classiers |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.6" data-path="../binaryclass/news20_adagrad.html"> |
| |
| <a href="../binaryclass/news20_adagrad.html"> |
| |
| |
| <b>6.3.6.</b> |
| |
| AdaGradRDA, AdaGrad, AdaDelta |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.7" data-path="../binaryclass/news20_rf.html"> |
| |
| <a href="../binaryclass/news20_rf.html"> |
| |
| |
| <b>6.3.7.</b> |
| |
| Random Forest |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.3.8" data-path="../binaryclass/news20b_xgboost.html"> |
| |
| <a href="../binaryclass/news20b_xgboost.html"> |
| |
| |
| <b>6.3.8.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.4" data-path="../binaryclass/kdd2010a.html"> |
| |
| <a href="../binaryclass/kdd2010a.html"> |
| |
| |
| <b>6.4.</b> |
| |
| KDD2010a Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.4.1" data-path="../binaryclass/kdd2010a_dataset.html"> |
| |
| <a href="../binaryclass/kdd2010a_dataset.html"> |
| |
| |
| <b>6.4.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.4.2" data-path="../binaryclass/kdd2010a_scw.html"> |
| |
| <a href="../binaryclass/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="../binaryclass/kdd2010b.html"> |
| |
| <a href="../binaryclass/kdd2010b.html"> |
| |
| |
| <b>6.5.</b> |
| |
| KDD2010b Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.5.1" data-path="../binaryclass/kdd2010b_dataset.html"> |
| |
| <a href="../binaryclass/kdd2010b_dataset.html"> |
| |
| |
| <b>6.5.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.5.2" data-path="../binaryclass/kdd2010b_arow.html"> |
| |
| <a href="../binaryclass/kdd2010b_arow.html"> |
| |
| |
| <b>6.5.2.</b> |
| |
| AROW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.6" data-path="../binaryclass/webspam.html"> |
| |
| <a href="../binaryclass/webspam.html"> |
| |
| |
| <b>6.6.</b> |
| |
| Webspam Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.6.1" data-path="../binaryclass/webspam_dataset.html"> |
| |
| <a href="../binaryclass/webspam_dataset.html"> |
| |
| |
| <b>6.6.1.</b> |
| |
| Data Pareparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.6.2" data-path="../binaryclass/webspam_scw.html"> |
| |
| <a href="../binaryclass/webspam_scw.html"> |
| |
| |
| <b>6.6.2.</b> |
| |
| PA1, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| |
| </ul> |
| |
| </li> |
| |
| <li class="chapter " data-level="6.7" data-path="../binaryclass/titanic_rf.html"> |
| |
| <a href="../binaryclass/titanic_rf.html"> |
| |
| |
| <b>6.7.</b> |
| |
| Kaggle Titanic Tutorial |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.8" data-path="../binaryclass/criteo.html"> |
| |
| <a href="../binaryclass/criteo.html"> |
| |
| |
| <b>6.8.</b> |
| |
| Criteo Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="6.8.1" data-path="../binaryclass/criteo_dataset.html"> |
| |
| <a href="../binaryclass/criteo_dataset.html"> |
| |
| |
| <b>6.8.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="6.8.2" data-path="../binaryclass/criteo_ffm.html"> |
| |
| <a href="../binaryclass/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="news20.html"> |
| |
| <a href="news20.html"> |
| |
| |
| <b>7.1.</b> |
| |
| News20 Multiclass Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="7.1.1" data-path="news20_dataset.html"> |
| |
| <a href="news20_dataset.html"> |
| |
| |
| <b>7.1.1.</b> |
| |
| Data Preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.2" data-path="news20_one-vs-the-rest_dataset.html"> |
| |
| <a href="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="news20_pa.html"> |
| |
| <a href="news20_pa.html"> |
| |
| |
| <b>7.1.3.</b> |
| |
| PA |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.4" data-path="news20_scw.html"> |
| |
| <a href="news20_scw.html"> |
| |
| |
| <b>7.1.4.</b> |
| |
| CW, AROW, SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.5" data-path="news20_xgboost.html"> |
| |
| <a href="news20_xgboost.html"> |
| |
| |
| <b>7.1.5.</b> |
| |
| XGBoost |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.6" data-path="news20_ensemble.html"> |
| |
| <a href="news20_ensemble.html"> |
| |
| |
| <b>7.1.6.</b> |
| |
| Ensemble learning |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.1.7" data-path="news20_one-vs-the-rest.html"> |
| |
| <a href="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="iris.html"> |
| |
| <a href="iris.html"> |
| |
| |
| <b>7.2.</b> |
| |
| Iris Tutorial |
| |
| </a> |
| |
| |
| |
| <ul class="articles"> |
| |
| |
| <li class="chapter " data-level="7.2.1" data-path="iris_dataset.html"> |
| |
| <a href="iris_dataset.html"> |
| |
| |
| <b>7.2.1.</b> |
| |
| Data preparation |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2.2" data-path="iris_scw.html"> |
| |
| <a href="iris_scw.html"> |
| |
| |
| <b>7.2.2.</b> |
| |
| SCW |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter active" data-level="7.2.3" data-path="iris_randomforest.html"> |
| |
| <a href="iris_randomforest.html"> |
| |
| |
| <b>7.2.3.</b> |
| |
| Random Forest |
| |
| </a> |
| |
| |
| |
| </li> |
| |
| <li class="chapter " data-level="7.2.4" data-path="iris_xgboost.html"> |
| |
| <a href="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="news20_dataset.html"> |
| |
| <a href="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> |
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| </nav> |
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| </div> |
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| <div class="book-body"> |
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| <div class="body-inner"> |
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| <!-- Title --> |
| <h1> |
| <i class="fa fa-circle-o-notch fa-spin"></i> |
| <a href=".." >Random Forest</a> |
| </h1> |
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| "License"); you may not use this file except in compliance |
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| <!-- toc --><div id="toc" class="toc"> |
| |
| <ul> |
| <li><a href="#dataset">Dataset</a></li> |
| <li><a href="#table-preparation">Table preparation</a></li> |
| <li><a href="#training">Training</a><ul> |
| <li><a href="#training-options">Training options</a><ul> |
| <li><a href="#parallelize-training">Parallelize Training</a></li> |
| <li><a href="#learning-stats">Learning stats</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li><a href="#prediction">Prediction</a><ul> |
| <li><a href="#parallelize-prediction">Parallelize Prediction</a></li> |
| </ul> |
| </li> |
| <li><a href="#evaluation">Evaluation</a></li> |
| <li><a href="#graphviz-export">Graphviz export</a></li> |
| </ul> |
| |
| </div><!-- tocstop --> |
| <h1 id="dataset">Dataset</h1> |
| <ul> |
| <li><a href="https://archive.ics.uci.edu/ml/datasets/Iris" target="_blank">https://archive.ics.uci.edu/ml/datasets/Iris</a></li> |
| </ul> |
| <pre><code>Attribute Information: |
| 1. sepal length in cm |
| 2. sepal width in cm |
| 3. petal length in cm |
| 4. petal width in cm |
| 5. class: |
| -- Iris Setosa |
| -- Iris Versicolour |
| -- Iris Virginica |
| </code></pre><h1 id="table-preparation">Table preparation</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">database</span> iris; |
| <span class="hljs-keyword">use</span> iris; |
| |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> <span class="hljs-keyword">raw</span> ( |
| sepal_length <span class="hljs-built_in">int</span>, |
| sepal_width <span class="hljs-built_in">int</span>, |
| petal_length <span class="hljs-built_in">int</span>, |
| petak_width <span class="hljs-built_in">int</span>, |
| <span class="hljs-keyword">class</span> <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/iris/raw'</span>; |
| |
| $ sed '/^$/d' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> label_mapping |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-keyword">class</span>, |
| <span class="hljs-keyword">rank</span> - <span class="hljs-number">1</span> <span class="hljs-keyword">as</span> label |
| <span class="hljs-keyword">from</span> ( |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-keyword">distinct</span> <span class="hljs-keyword">class</span>, |
| <span class="hljs-keyword">dense_rank</span>() <span class="hljs-keyword">over</span> (<span class="hljs-keyword">order</span> <span class="hljs-keyword">by</span> <span class="hljs-keyword">class</span>) <span class="hljs-keyword">as</span> <span class="hljs-keyword">rank</span> |
| <span class="hljs-keyword">from</span> |
| <span class="hljs-keyword">raw</span> |
| ) t |
| ; |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> training |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">select</span> |
| <span class="hljs-keyword">rowid</span>() <span class="hljs-keyword">as</span> <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-built_in">array</span>(t1.sepal_length, t1.sepal_width, t1.petal_length, t1.petak_width) <span class="hljs-keyword">as</span> features, |
| t2.label |
| <span class="hljs-keyword">from</span> |
| <span class="hljs-keyword">raw</span> t1 |
| <span class="hljs-keyword">JOIN</span> label_mapping t2 <span class="hljs-keyword">ON</span> (t1.<span class="hljs-keyword">class</span> = t2.<span class="hljs-keyword">class</span>) |
| ; |
| </code></pre> |
| <h1 id="training">Training</h1> |
| <p><code>train_randomforest_classifier</code> takes a dense <code>features</code> in double[] and a <code>label</code> starting from 0.</p> |
| <pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">model</span> |
| <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> SEQUENCEFILE |
| <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">select</span> |
| train_randomforest_classifier(features, label) |
| <span class="hljs-comment">-- v0.5.0 and later</span> |
| <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (model_id, model_weight, model, var_importance, oob_errors, oob_tests)</span> |
| <span class="hljs-comment">-- v0.4.1-alpha.2 and before</span> |
| <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (pred_model, var_importance, oob_errors, oob_tests)</span> |
| <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> |
| <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests)</span> |
| <span class="hljs-keyword">from</span> |
| training; |
| </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>Note that model storage format is different between versions as seen the above.</p></div></div> |
| <pre><code class="lang-sql">hive> desc extended model; |
| </code></pre> |
| <table> |
| <thead> |
| <tr> |
| <th style="text-align:center">col_name</th> |
| <th style="text-align:center">data_type </th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td style="text-align:center">model_id</td> |
| <td style="text-align:center">string</td> |
| </tr> |
| <tr> |
| <td style="text-align:center">model_weight</td> |
| <td style="text-align:center">double</td> |
| </tr> |
| <tr> |
| <td style="text-align:center">model</td> |
| <td style="text-align:center">string</td> |
| </tr> |
| <tr> |
| <td style="text-align:center">var_importance</td> |
| <td style="text-align:center">array<double></double></td> |
| </tr> |
| <tr> |
| <td style="text-align:center">oob_errors</td> |
| <td style="text-align:center">int</td> |
| </tr> |
| <tr> |
| <td style="text-align:center">oob_tests</td> |
| <td style="text-align:center">int</td> |
| </tr> |
| </tbody> |
| </table> |
| <h2 id="training-options">Training options</h2> |
| <p><code>-help</code> option shows usage of the function.</p> |
| <pre><code class="lang-sql">select train_randomforest_classifier(features, label, "-help") from training; |
| |
| > FAILED: UDFArgumentException |
| usage: train_randomforest_classifier(array<double|string> features, int |
| label [, const array<double> classWeights, const string options]) - |
| Returns a relation consists of <int model_id, int model_type, |
| string pred_model, array<double> var_importance, int oob_errors, |
| int oob_tests, double weight> [-attrs <arg>] [-depth <arg>] [-help] |
| [-leafs <arg>] [-min_samples_leaf <arg>] [-rule <arg>] [-seed |
| <arg>] [-splits <arg>] [-stratified] [-subsample <arg>] [-trees |
| <arg>] [-vars <arg>] |
| -attrs,--attribute_types <arg> Comma separated attribute types (Q |
| for quantitative variable and C for |
| categorical variable. e.g., |
| [Q,C,Q,C]) |
| -depth,--max_depth <arg> The maximum number of the tree depth |
| [default: Integer.MAX_VALUE] |
| -help Show function help |
| -leafs,--max_leaf_nodes <arg> The maximum number of leaf nodes |
| [default: Integer.MAX_VALUE] |
| -min_samples_leaf <arg> The minimum number of samples in a |
| leaf node [default: 1] |
| -rule,--split_rule <arg> Split algorithm [default: GINI, |
| ENTROPY] |
| -seed <arg> seed value in long [default: -1 |
| (random)] |
| -splits,--min_split <arg> A node that has greater than or |
| equals to `min_split` examples will |
| split [default: 2] |
| -stratified,--stratified_sampling Enable Stratified sampling for |
| unbalanced data |
| -subsample <arg> Sampling rate in range (0.0,1.0] |
| -trees,--num_trees <arg> The number of trees for each task |
| [default: 50] |
| -vars,--num_variables <arg> The number of random selected |
| features [default: |
| ceil(sqrt(x[0].length))]. |
| int(num_variables * x[0].length) is |
| considered if num_variable is (0,1] |
| </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>-num_trees</code> controls the number of trees for each task, not the total number of trees.</p></div></div> |
| <h3 id="parallelize-training">Parallelize Training</h3> |
| <p>To parallelize RandomForest training, you can use UNION ALL as follows:</p> |
| <pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">model</span> |
| <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-comment">-- STORED AS SEQUENCEFILE </span> |
| <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">select</span> |
| train_randomforest_classifier(features, label, <span class="hljs-string">'-trees 25'</span>) |
| <span class="hljs-keyword">from</span> |
| training |
| <span class="hljs-keyword">UNION</span> ALL |
| <span class="hljs-keyword">select</span> |
| train_randomforest_classifier(features, label, <span class="hljs-string">'-trees 25'</span>) |
| <span class="hljs-keyword">from</span> |
| training |
| ; |
| </code></pre> |
| <h3 id="learning-stats">Learning stats</h3> |
| <p><a href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#varimp" target="_blank"><code>Variable importance</code></a> and <a href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr" target="_blank"><code>Out Of Bag (OOB) error rate</code></a> of RandomForest can be shown as follows:</p> |
| <pre><code class="lang-sql"><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> |
| <span class="hljs-keyword">model</span>; |
| </code></pre> |
| <blockquote> |
| <p>[6.837674865013268,4.1317115752776665,24.331571871930226,25.677497925673062] 0.056666666666666664</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">create</span> <span class="hljs-keyword">table</span> predicted |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">SELECT</span> |
| <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> |
| <span class="hljs-comment">-- v0.5.0 or later</span> |
| 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> |
| <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> |
| <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> |
| <span class="hljs-comment">-- v0.5.0 or later</span> |
| 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">model</span> p |
| <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> |
| training t |
| ) t1 |
| <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> |
| <span class="hljs-keyword">rowid</span> |
| ; |
| </code></pre> |
| <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>Left outer join without a join condition (i.e., <code>model p LEFT OUTER JOIN training t</code>) is a trick to fix the left table for cross join.</p><h4 id="caution">Caution</h4><p><code>tree_predict_v1</code> is for the backward compatibility for using prediction models built before <code>v0.5</code> on <code>v0.5</code> or later.</p></div></div> |
| <h3 id="parallelize-prediction">Parallelize Prediction</h3> |
| <p>The following query runs predictions in N-parallel. It would reduce elapsed time for prediction almost by N.</p> |
| <pre><code class="lang-sql"><span class="hljs-keyword">SET</span> hivevar:classification=<span class="hljs-literal">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">8</span>; |
| |
| <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted; |
| <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted |
| <span class="hljs-keyword">as</span> |
| <span class="hljs-keyword">SELECT</span> |
| <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> |
| <span class="hljs-comment">-- v0.5.0 or later</span> |
| 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.<span class="hljs-keyword">rowid</span>, |
| <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> |
| <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> |
| <span class="hljs-comment">-- v0.5.0 or later</span> |
| 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 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> |
| <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> |
| <span class="hljs-comment">-- model_id, model_type, pred_model</span> |
| <span class="hljs-comment">-- v0.5.0 or later</span> |
| model_id, model_weight, <span class="hljs-keyword">model</span> |
| <span class="hljs-keyword">FROM</span> <span class="hljs-keyword">model</span> |
| <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> training t |
| ) t1 |
| <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> |
| <span class="hljs-keyword">rowid</span>; |
| </code></pre> |
| <h1 id="evaluation">Evaluation</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">select</span> <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">from</span> training; |
| </code></pre> |
| <blockquote> |
| <p>150</p> |
| </blockquote> |
| <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:total_cnt=<span class="hljs-number">150</span>; |
| |
| WITH t1 as ( |
| <span class="hljs-keyword">SELECT</span> |
| t.<span class="hljs-keyword">rowid</span>, |
| t.label <span class="hljs-keyword">as</span> actual, |
| p.predicted.label <span class="hljs-keyword">as</span> predicted |
| <span class="hljs-keyword">FROM</span> |
| predicted p |
| <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> training t <span class="hljs-keyword">ON</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>) |
| ) |
| <span class="hljs-keyword">SELECT</span> |
| <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) / ${total_cnt} |
| <span class="hljs-keyword">FROM</span> |
| t1 |
| <span class="hljs-keyword">WHERE</span> |
| actual = predicted |
| ; |
| </code></pre> |
| <blockquote> |
| <p>0.98</p> |
| </blockquote> |
| <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"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'sepal_length'</span>,<span class="hljs-string">'sepal_width'</span>,<span class="hljs-string">'petal_length'</span>,<span class="hljs-string">'petak_width'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'Setosa'</span>,<span class="hljs-string">'Versicolour'</span>,<span class="hljs-string">'Virginica'</span>)) <span class="hljs-keyword">as</span> js, |
| tree_export(<span class="hljs-keyword">model</span>, <span class="hljs-string">"-type graphviz"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'sepal_length'</span>,<span class="hljs-string">'sepal_width'</span>,<span class="hljs-string">'petal_length'</span>,<span class="hljs-string">'petak_width'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'Setosa'</span>,<span class="hljs-string">'Versicolour'</span>,<span class="hljs-string">'Virginica'</span>)) <span class="hljs-keyword">as</span> dot |
| <span class="hljs-keyword">from</span> |
| <span class="hljs-keyword">model</span> |
| <span class="hljs-comment">-- limit 1</span> |
| ; |
| </code></pre> |
| <pre><code>digraph Tree { |
| node [shape=box, style="filled, rounded", color="black", fontname=helvetica]; |
| edge [fontname=helvetica]; |
| 0 [label=<petal_length &le; 2.599999964237213>, fillcolor="#00000000"]; |
| 1 [label=<predicted = Setosa>, fillcolor="0.0000,1.000,1.000", shape=ellipse]; |
| 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"]; |
| 2 [label=<petal_length &le; 4.950000047683716>, fillcolor="#00000000"]; |
| 0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"]; |
| 3 [label=<petak_width &le; 1.6500000357627869>, fillcolor="#00000000"]; |
| 2 -> 3; |
| 4 [label=<predicted = Versicolour>, fillcolor="0.3333,1.000,1.000", shape=ellipse]; |
| 3 -> 4; |
| 5 [label=<sepal_width &le; 3.100000023841858>, fillcolor="#00000000"]; |
| 3 -> 5; |
| 6 [label=<predicted = Virginica>, fillcolor="0.6667,1.000,1.000", shape=ellipse]; |
| 5 -> 6; |
| 7 [label=<predicted = Versicolour>, fillcolor="0.3333,1.000,1.000", shape=ellipse]; |
| 5 -> 7; |
| 8 [label=<petak_width &le; 1.75>, fillcolor="#00000000"]; |
| 2 -> 8; |
| 9 [label=<petal_length &le; 5.299999952316284>, fillcolor="#00000000"]; |
| 8 -> 9; |
| 10 [label=<predicted = Versicolour>, fillcolor="0.3333,1.000,1.000", shape=ellipse]; |
| 9 -> 10; |
| 11 [label=<predicted = Virginica>, fillcolor="0.6667,1.000,1.000", shape=ellipse]; |
| 9 -> 11; |
| 12 [label=<predicted = Virginica>, fillcolor="0.6667,1.000,1.000", shape=ellipse]; |
| 8 -> 12; |
| } |
| </code></pre><p><img src="../resources/images/iris.png" alt="Iris Graphviz output"></p> |
| <p>You can draw a graph by <code>dot -Tpng iris.dot -o iris.png</code> or using <a href="https://viz-js.com/" target="_blank">Viz.js</a>.</p> |
| <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, |
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