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| <title>Field-Aware Factorization Machines ยท Hivemall User Manual</title> |
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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="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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| </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 " 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 active" 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> |
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| <!-- Title --> |
| <h1> |
| <i class="fa fa-circle-o-notch fa-spin"></i> |
| <a href=".." >Field-Aware Factorization Machines</a> |
| </h1> |
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| to you under the Apache License, Version 2.0 (the |
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| <p><a href="https://dl.acm.org/citation.cfm?id=2959134" target="_blank">Field-aware factorization machines</a> (FFM) is a factorization model which has been used by the <a href="https://www.kaggle.com/c/criteo-display-ad-challenge/discussion/10555" target="_blank">#1 solution</a> of the Criteo competition.</p> |
| <p>This page guides you to try the factorization technique with Hivemall's <code>train_ffm</code> and <code>ffm_predict</code> UDFs.</p> |
| <!-- toc --><div id="toc" class="toc"> |
| |
| <ul> |
| <li><a href="#preprocess-data-and-convert-into-libffm-format">Preprocess data and convert into LIBFFM format</a></li> |
| <li><a href="#insert-preprocessed-data-into-tables">Insert preprocessed data into tables</a></li> |
| <li><a href="#training">Training</a></li> |
| <li><a href="#prediction-and-evaluation">Prediction and evaluation</a></li> |
| </ul> |
| |
| </div><!-- tocstop --> |
| <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>This feature is supported from Hivemall v0.5.1 or later.</p></div></div> |
| <h1 id="preprocess-data-and-convert-into-libffm-format">Preprocess data and convert into LIBFFM format</h1> |
| <p>Since FFM is a relatively complex factor-based model which requires us to spend a significant amount of time for feature engineering, preprocessing data outside of Hive can be a reasonable option.</p> |
| <p>You can again use the repository <strong><a href="https://github.com/takuti/criteo-ffm" target="_blank">takuti/criteo-ffm</a></strong> cloned in the <a href="criteo_dataset.html">data preparation guide</a> to preprocess the data as the winning solution did:</p> |
| <pre><code class="lang-sh"><span class="hljs-built_in">cd</span> criteo-ffm |
| <span class="hljs-comment"># create the CSV files `tr.csv` and `te.csv`</span> |
| make preprocess |
| </code></pre> |
| <p>Task <code>make preprocess</code> executes some Python scripts which are originally taken from <a href="https://github.com/guestwalk/kaggle-2014-criteo" target="_blank">guestwalk/kaggle-2014-criteo</a> and <a href="https://github.com/chenhuang-learn/ffm" target="_blank">chenhuang-learn/ffm</a>.</p> |
| <p>Eventually, you will obtain the following files in so-called LIBFFM format:</p> |
| <ul> |
| <li><code>tr.ffm</code> - Labeled training samples<ul> |
| <li><code>tr.sp</code> - 80% of the labeled training samples randomly picked from <code>tr.ffm</code></li> |
| <li><code>va.sp</code> - Remaining 20% of samples for evaluation</li> |
| </ul> |
| </li> |
| <li><code>te.ffm</code> - Unlabeled test samples</li> |
| </ul> |
| <pre><code><label> <field1>:<feature1>:<value1> <field2>:<feature2>:<value2> ... |
| . |
| . |
| . |
| </code></pre><p>See <a href="https://github.com/guestwalk/libffm" target="_blank">LIBFFM official README</a> for detail.</p> |
| <p>In order to evaluate the accuracy of prediction at the end of this tutorial, later sections use <code>tr.sp</code> and <code>va.sp</code>.</p> |
| <h1 id="insert-preprocessed-data-into-tables">Insert preprocessed data into tables</h1> |
| <p>Create new tables used by the FFM UDFs:</p> |
| <pre><code class="lang-sh">hadoop fs -put tr.sp /criteo/ffm/train |
| hadoop fs -put va.sp /criteo/ffm/<span class="hljs-built_in">test</span> |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">use</span> criteo; |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> train_ffm; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">EXTERNAL</span> <span class="hljs-keyword">TABLE</span> train_ffm ( |
| label <span class="hljs-built_in">int</span>, |
| <span class="hljs-comment">-- quantitative features</span> |
| i1 <span class="hljs-keyword">string</span>,i2 <span class="hljs-keyword">string</span>,i3 <span class="hljs-keyword">string</span>,i4 <span class="hljs-keyword">string</span>,i5 <span class="hljs-keyword">string</span>,i6 <span class="hljs-keyword">string</span>,i7 <span class="hljs-keyword">string</span>,i8 <span class="hljs-keyword">string</span>,i9 <span class="hljs-keyword">string</span>,i10 <span class="hljs-keyword">string</span>,i11 <span class="hljs-keyword">string</span>,i12 <span class="hljs-keyword">string</span>,i13 <span class="hljs-keyword">string</span>, |
| <span class="hljs-comment">-- categorical features</span> |
| c1 <span class="hljs-keyword">string</span>,c2 <span class="hljs-keyword">string</span>,c3 <span class="hljs-keyword">string</span>,c4 <span class="hljs-keyword">string</span>,c5 <span class="hljs-keyword">string</span>,c6 <span class="hljs-keyword">string</span>,c7 <span class="hljs-keyword">string</span>,c8 <span class="hljs-keyword">string</span>,c9 <span class="hljs-keyword">string</span>,c10 <span class="hljs-keyword">string</span>,c11 <span class="hljs-keyword">string</span>,c12 <span class="hljs-keyword">string</span>,c13 <span class="hljs-keyword">string</span>,c14 <span class="hljs-keyword">string</span>,c15 <span class="hljs-keyword">string</span>,c16 <span class="hljs-keyword">string</span>,c17 <span class="hljs-keyword">string</span>,c18 <span class="hljs-keyword">string</span>,c19 <span class="hljs-keyword">string</span>,c20 <span class="hljs-keyword">string</span>,c21 <span class="hljs-keyword">string</span>,c22 <span class="hljs-keyword">string</span>,c23 <span class="hljs-keyword">string</span>,c24 <span class="hljs-keyword">string</span>,c25 <span class="hljs-keyword">string</span>,c26 <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">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/criteo/ffm/train'</span>; |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> test_ffm; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">EXTERNAL</span> <span class="hljs-keyword">TABLE</span> test_ffm ( |
| label <span class="hljs-built_in">int</span>, |
| <span class="hljs-comment">-- quantitative features</span> |
| i1 <span class="hljs-keyword">string</span>,i2 <span class="hljs-keyword">string</span>,i3 <span class="hljs-keyword">string</span>,i4 <span class="hljs-keyword">string</span>,i5 <span class="hljs-keyword">string</span>,i6 <span class="hljs-keyword">string</span>,i7 <span class="hljs-keyword">string</span>,i8 <span class="hljs-keyword">string</span>,i9 <span class="hljs-keyword">string</span>,i10 <span class="hljs-keyword">string</span>,i11 <span class="hljs-keyword">string</span>,i12 <span class="hljs-keyword">string</span>,i13 <span class="hljs-keyword">string</span>, |
| <span class="hljs-comment">-- categorical features</span> |
| c1 <span class="hljs-keyword">string</span>,c2 <span class="hljs-keyword">string</span>,c3 <span class="hljs-keyword">string</span>,c4 <span class="hljs-keyword">string</span>,c5 <span class="hljs-keyword">string</span>,c6 <span class="hljs-keyword">string</span>,c7 <span class="hljs-keyword">string</span>,c8 <span class="hljs-keyword">string</span>,c9 <span class="hljs-keyword">string</span>,c10 <span class="hljs-keyword">string</span>,c11 <span class="hljs-keyword">string</span>,c12 <span class="hljs-keyword">string</span>,c13 <span class="hljs-keyword">string</span>,c14 <span class="hljs-keyword">string</span>,c15 <span class="hljs-keyword">string</span>,c16 <span class="hljs-keyword">string</span>,c17 <span class="hljs-keyword">string</span>,c18 <span class="hljs-keyword">string</span>,c19 <span class="hljs-keyword">string</span>,c20 <span class="hljs-keyword">string</span>,c21 <span class="hljs-keyword">string</span>,c22 <span class="hljs-keyword">string</span>,c23 <span class="hljs-keyword">string</span>,c24 <span class="hljs-keyword">string</span>,c25 <span class="hljs-keyword">string</span>,c26 <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">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/criteo/ffm/test'</span>; |
| </code></pre> |
| <p>Vectorize the LIBFFM-formatted features with <code>rowid</code>:</p> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> train_vectorized; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> train_vectorized <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">SELECT</span> |
| row_number() <span class="hljs-keyword">OVER</span> () <span class="hljs-keyword">AS</span> <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-built_in">array</span>( |
| i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12, i13, |
| c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26 |
| ) <span class="hljs-keyword">AS</span> features, |
| label |
| <span class="hljs-keyword">FROM</span> |
| train_ffm |
| ; |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> test_vectorized; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> test_vectorized <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">SELECT</span> |
| row_number() <span class="hljs-keyword">OVER</span> () <span class="hljs-keyword">AS</span> <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-built_in">array</span>( |
| i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12, i13, |
| c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26 |
| ) <span class="hljs-keyword">AS</span> features, |
| label |
| <span class="hljs-keyword">FROM</span> |
| test_ffm |
| ; |
| </code></pre> |
| <h1 id="training">Training</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> criteo.ffm_model; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> criteo.ffm_model ( |
| model_id <span class="hljs-built_in">int</span>, |
| i <span class="hljs-built_in">int</span>, |
| Wi <span class="hljs-built_in">float</span>, |
| Vi <span class="hljs-built_in">array</span><<span class="hljs-built_in">float</span>> |
| ); |
| </code></pre> |
| <pre><code class="lang-sql"><span class="hljs-keyword">INSERT</span> OVERWRITE <span class="hljs-keyword">TABLE</span> criteo.ffm_model |
| <span class="hljs-keyword">SELECT</span> |
| train_ffm( |
| features, |
| label, |
| <span class="hljs-string">'-init_v random -max_init_value 0.5 -classification -iterations 15 -factors 4 -eta 0.2 -optimizer adagrad -lambda 0.00002'</span> |
| ) |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| features, label |
| <span class="hljs-keyword">FROM</span> |
| criteo.train_vectorized |
| CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) |
| ) t |
| ; |
| </code></pre> |
| <p>The third argument of <code>train_ffm</code> accepts a variety of options:</p> |
| <pre><code>hive> SELECT train_ffm(array(), 0, '-help'); |
| usage: train_ffm(array<string> x, double y [, const string options]) - |
| Returns a prediction model [-alpha <arg>] [-auto_stop] [-beta |
| <arg>] [-c] [-cv_rate <arg>] [-disable_cv] [-enable_norm] |
| [-enable_wi] [-eps <arg>] [-eta <arg>] [-eta0 <arg>] [-f <arg>] |
| [-feature_hashing <arg>] [-help] [-init_v <arg>] [-int_feature] |
| [-iters <arg>] [-l1 <arg>] [-l2 <arg>] [-lambda0 <arg>] [-lambdaV |
| <arg>] [-lambdaW0 <arg>] [-lambdaWi <arg>] [-max <arg>] [-maxval |
| <arg>] [-min <arg>] [-min_init_stddev <arg>] [-no_norm] |
| [-num_fields <arg>] [-opt <arg>] [-p <arg>] [-power_t <arg>] [-seed |
| <arg>] [-sigma <arg>] [-t <arg>] [-va_ratio <arg>] [-va_threshold |
| <arg>] [-w0] |
| -alpha,--alphaFTRL <arg> Alpha value (learning rate) |
| of |
| Follow-The-Regularized-Reade |
| r [default: 0.2] |
| -auto_stop,--early_stopping Stop at the iteration that |
| achieves the best validation |
| on partial samples [default: |
| OFF] |
| -beta,--betaFTRL <arg> Beta value (a learning |
| smoothing parameter) of |
| Follow-The-Regularized-Reade |
| r [default: 1.0] |
| -c,--classification Act as classification |
| -cv_rate,--convergence_rate <arg> Threshold to determine |
| convergence [default: 0.005] |
| -disable_cv,--disable_cvtest Whether to disable |
| convergence check [default: |
| OFF] |
| -enable_norm,--l2norm Enable instance-wise L2 |
| normalization |
| -enable_wi,--linear_term Include linear term |
| [default: OFF] |
| -eps <arg> A constant used in the |
| denominator of AdaGrad |
| [default: 1.0] |
| -eta <arg> The initial learning rate |
| -eta0 <arg> The initial learning rate |
| [default 0.1] |
| -f,--factors <arg> The number of the latent |
| variables [default: 5] |
| -feature_hashing <arg> The number of bits for |
| feature hashing in range |
| [18,31] [default: -1]. No |
| feature hashing for -1. |
| -help Show function help |
| -init_v <arg> Initialization strategy of |
| matrix V [random, |
| gaussian](default: 'random' |
| for regression / 'gaussian' |
| for classification) |
| -int_feature,--feature_as_integer Parse a feature as integer |
| [default: OFF] |
| -iters,--iterations <arg> The number of iterations |
| [default: 10] |
| -l1,--lambda1 <arg> L1 regularization value of |
| Follow-The-Regularized-Reade |
| r that controls model |
| Sparseness [default: 0.001] |
| -l2,--lambda2 <arg> L2 regularization value of |
| Follow-The-Regularized-Reade |
| r [default: 0.0001] |
| -lambda0,--lambda <arg> The initial lambda value for |
| regularization [default: |
| 0.0001] |
| -lambdaV,--lambda_v <arg> The initial lambda value for |
| V regularization [default: |
| 0.0001] |
| -lambdaW0,--lambda_w0 <arg> The initial lambda value for |
| W0 regularization [default: |
| 0.0001] |
| -lambdaWi,--lambda_wi <arg> The initial lambda value for |
| Wi regularization [default: |
| 0.0001] |
| -max,--max_target <arg> The maximum value of target |
| variable |
| -maxval,--max_init_value <arg> The maximum initial value in |
| the matrix V [default: 0.5] |
| -min,--min_target <arg> The minimum value of target |
| variable |
| -min_init_stddev <arg> The minimum standard |
| deviation of initial matrix |
| V [default: 0.1] |
| -no_norm,--disable_norm Disable instance-wise L2 |
| normalization |
| -num_fields <arg> The number of fields |
| [default: 256] |
| -opt,--optimizer <arg> Gradient Descent optimizer |
| [default: ftrl, adagrad, |
| sgd] |
| -p,--num_features <arg> The size of feature |
| dimensions [default: -1] |
| -power_t <arg> The exponent for inverse |
| scaling learning rate |
| [default 0.1] |
| -seed <arg> Seed value [default: -1 |
| (random)] |
| -sigma <arg> The standard deviation for |
| initializing V [default: |
| 0.1] |
| -t,--total_steps <arg> The total number of training |
| examples |
| -va_ratio,--validation_ratio <arg> Ratio of training data used |
| for validation [default: |
| 0.05f] |
| -va_threshold,--validation_threshold <arg> Threshold to start |
| validation. At least N |
| training examples are used |
| before validation [default: |
| 1000] |
| -w0,--global_bias Whether to include global |
| bias term w0 [default: OFF] |
| </code></pre><p>Note that debug log describes the change of cumulative loss over iterations as follows:</p> |
| <pre><code>Iteration #2 | average loss=0.5407147187026483, current cumulative loss=858.114258581103, previous cumulative loss=1682.1101438997914, change rate=0.48985846040280256, #trainingExamples=1587 |
| Iteration #3 | average loss=0.5105058761578417, current cumulative loss=810.1728254624949, previous cumulative loss=858.114258581103, change rate=0.05586835626980435, #trainingExamples=1587 |
| Iteration #4 | average loss=0.49045915570992393, current cumulative loss=778.3586801116493, previous cumulative loss=810.1728254624949, change rate=0.039268344174200345, #trainingExamples=1587 |
| Iteration #5 | average loss=0.4752751205770395, current cumulative loss=754.2616163557617, previous cumulative loss=778.3586801116493, change rate=0.030958816766109738, #trainingExamples=1587 |
| Iteration #6 | average loss=0.46308523885164105, current cumulative loss=734.9162740575543, previous cumulative loss=754.2616163557617, change rate=0.02564805351182389, #trainingExamples=1587 |
| Iteration #7 | average loss=0.4529012395753083, current cumulative loss=718.7542672060143, previous cumulative loss=734.9162740575543, change rate=0.02199163009727323, #trainingExamples=1587 |
| Iteration #8 | average loss=0.44411358945347845, current cumulative loss=704.8082664626703, previous cumulative loss=718.7542672060143, change rate=0.019403016273636577, #trainingExamples=1587 |
| Iteration #9 | average loss=0.4363264696377158, current cumulative loss=692.450107315055, previous cumulative loss=704.8082664626703, change rate=0.017534072365012268, #trainingExamples=1587 |
| Iteration #10 | average loss=0.4292753045556725, current cumulative loss=681.2599083298522, previous cumulative loss=692.450107315055, change rate=0.01616029641267912, #trainingExamples=1587 |
| Iteration #11 | average loss=0.42277515600757143, current cumulative loss=670.9441725840159, previous cumulative loss=681.2599083298522, change rate=0.015142144165104322, #trainingExamples=1587 |
| Iteration #12 | average loss=0.416689617663307, current cumulative loss=661.2864232316682, previous cumulative loss=670.9441725840159, change rate=0.014394266687126348, #trainingExamples=1587 |
| Iteration #13 | average loss=0.4109140194740033, current cumulative loss=652.1205489052433, previous cumulative loss=661.2864232316682, change rate=0.013860672175351585, #trainingExamples=1587 |
| Iteration #14 | average loss=0.4053667348634373, current cumulative loss=643.317008228275, previous cumulative loss=652.1205489052433, change rate=0.013499866998129951, #trainingExamples=1587 |
| Iteration #15 | average loss=0.3999840450561501, current cumulative loss=634.7746795041102, previous cumulative loss=643.317008228275, change rate=0.013278568131893133, #trainingExamples=1587 |
| Performed 15 iterations of 1,587 training examples on memory (thus 23,805 training updates in total) |
| </code></pre><h1 id="prediction-and-evaluation">Prediction and evaluation</h1> |
| <pre><code class="lang-sql"><span class="hljs-keyword">DROP</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">EXISTS</span> criteo.test_exploded; |
| <span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> criteo.test_exploded <span class="hljs-keyword">AS</span> |
| <span class="hljs-keyword">SELECT</span> |
| t1.<span class="hljs-keyword">rowid</span>, |
| t2.i, |
| t2.j, |
| t2.Xi, |
| t2.Xj |
| <span class="hljs-keyword">from</span> |
| criteo.test_vectorized t1 |
| LATERAL <span class="hljs-keyword">VIEW</span> feature_pairs(t1.features, <span class="hljs-string">'-ffm'</span>) t2 <span class="hljs-keyword">AS</span> i, j, Xi, Xj |
| ; |
| </code></pre> |
| <pre><code class="lang-sql">WITH predicted AS ( |
| <span class="hljs-keyword">SELECT</span> |
| <span class="hljs-keyword">rowid</span>, |
| <span class="hljs-keyword">avg</span>(score) <span class="hljs-keyword">AS</span> predicted |
| <span class="hljs-keyword">FROM</span> ( |
| <span class="hljs-keyword">SELECT</span> |
| t1.<span class="hljs-keyword">rowid</span>, |
| p1.model_id, |
| sigmoid(ffm_predict(p1.Wi, p1.Vi, p2.Vi, t1.Xi, t1.Xj)) <span class="hljs-keyword">AS</span> score |
| <span class="hljs-keyword">FROM</span> |
| criteo.test_exploded t1 |
| <span class="hljs-keyword">JOIN</span> criteo.ffm_model p1 <span class="hljs-keyword">ON</span> (p1.i = t1.i) <span class="hljs-comment">-- at least p1.i = 0 and t1.i = 0 exists</span> |
| <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> criteo.ffm_model p2 <span class="hljs-keyword">ON</span> (p2.model_id = p1.model_id <span class="hljs-keyword">and</span> p2.i = t1.j) |
| <span class="hljs-keyword">WHERE</span> |
| p1.Wi <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">null</span> <span class="hljs-keyword">OR</span> p2.Vi <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">null</span> |
| <span class="hljs-keyword">GROUP</span> <span class="hljs-keyword">BY</span> |
| t1.<span class="hljs-keyword">rowid</span>, p1.model_id |
| ) t |
| <span class="hljs-keyword">GROUP</span> <span class="hljs-keyword">BY</span> |
| <span class="hljs-keyword">rowid</span> |
| ) |
| <span class="hljs-keyword">SELECT</span> |
| logloss(t1.predicted, t2.label) |
| <span class="hljs-keyword">FROM</span> |
| predicted t1 |
| <span class="hljs-keyword">JOIN</span> |
| criteo.test_vectorized t2 |
| <span class="hljs-keyword">ON</span> t1.<span class="hljs-keyword">rowid</span> = t2.<span class="hljs-keyword">rowid</span> |
| ; |
| </code></pre> |
| <blockquote> |
| <p>0.47276208106423234</p> |
| </blockquote> |
| <p><br></p> |
| <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>The accuracy varies depending on the random separation of <code>tr.sp</code> and <code>va.sp</code>.</p></div></div> |
| <p>Notice that LogLoss around 0.45 is reasonable accuracy compared to the <a href="https://github.com/guestwalk/libffm" target="_blank">competition leaderboard</a> and output from <a href="https://github.com/guestwalk/libffm" target="_blank">LIBFFM</a>. |
| <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. |
| --> |
| <p><sub><font color="gray"> |
| Apache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. |
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