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class="divider"></li> + + + + + <li class="header">TABLE OF CONTENTS</li> + + + + <li class="chapter " data-level="1.1" data-path="../"> + + <a href="../"> + + + <b>1.1.</b> + + Introduction + + </a> + + + + </li> + + <li class="chapter " data-level="1.2" data-path="../getting_started/"> + + <a href="../getting_started/"> + + + <b>1.2.</b> + + Getting Started + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.2.1" data-path="../getting_started/installation.html"> + + <a href="../getting_started/installation.html"> + + + <b>1.2.1.</b> + + Installation + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.2" data-path="../getting_started/permanent-functions.html"> + + <a href="../getting_started/permanent-functions.html"> + + + <b>1.2.2.</b> + + Install as permanent functions + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.3" data-path="../getting_started/input-format.html"> + + <a href="../getting_started/input-format.html"> + + + <b>1.2.3.</b> + + Input Format + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.3" data-path="../tips/"> + + <a href="../tips/"> + + + <b>1.3.</b> + + Tips for Effective Hivemall + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.3.1" data-path="../tips/addbias.html"> + + <a href="../tips/addbias.html"> + + + <b>1.3.1.</b> + + Explicit addBias() for better prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.2" data-path="../tips/rand_amplify.html"> + + <a href="../tips/rand_amplify.html"> + + + <b>1.3.2.</b> + + Use rand_amplify() to better prediction results + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.3" data-path="../tips/rt_prediction.html"> + + <a href="../tips/rt_prediction.html"> + + + <b>1.3.3.</b> + + Real-time Prediction on RDBMS + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.4" data-path="../tips/ensemble_learning.html"> + + <a href="../tips/ensemble_learning.html"> + + + <b>1.3.4.</b> + + Ensemble learning for stable prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.5" data-path="../tips/mixserver.html"> + + <a href="../tips/mixserver.html"> + + + <b>1.3.5.</b> + + Mixing models for a better prediction convergence (MIX server) + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.6" data-path="../tips/emr.html"> + + <a href="../tips/emr.html"> + + + <b>1.3.6.</b> + + Run Hivemall on Amazon Elastic MapReduce + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.4" data-path="../tips/general_tips.html"> + + <a href="../tips/general_tips.html"> + + + <b>1.4.</b> + + General Hive/Hadoop tips + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.4.1" data-path="../tips/rowid.html"> + + <a href="../tips/rowid.html"> + + + <b>1.4.1.</b> + + Adding rowid for each row + + </a> + + + + </li> + + <li class="chapter " data-level="1.4.2" data-path="../tips/hadoop_tuning.html"> + + <a href="../tips/hadoop_tuning.html"> + + + <b>1.4.2.</b> + + Hadoop tuning for Hivemall + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.5" data-path="../troubleshooting/"> + + <a href="../troubleshooting/"> + + + <b>1.5.</b> + + Troubleshooting + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.5.1" data-path="../troubleshooting/oom.html"> + + <a href="../troubleshooting/oom.html"> + + + <b>1.5.1.</b> + + OutOfMemoryError in training + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.2" data-path="../troubleshooting/mapjoin_task_error.html"> + + <a href="../troubleshooting/mapjoin_task_error.html"> + + + <b>1.5.2.</b> + + SemanticException Generate Map Join Task Error: Cannot serialize object + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.3" data-path="../troubleshooting/asterisk.html"> + + <a href="../troubleshooting/asterisk.html"> + + + <b>1.5.3.</b> + + Asterisk argument for UDTF does not work + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.4" data-path="../troubleshooting/num_mappers.html"> + + <a href="../troubleshooting/num_mappers.html"> + + + <b>1.5.4.</b> + + The number of mappers is less than input splits in Hadoop 2.x + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.5" data-path="../troubleshooting/mapjoin_classcastex.html"> + + <a href="../troubleshooting/mapjoin_classcastex.html"> + + + <b>1.5.5.</b> + + Map-side Join causes ClassCastException on Tez + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part II - Generic Features</li> + + + + <li class="chapter " data-level="2.1" data-path="../misc/generic_funcs.html"> + + <a href="../misc/generic_funcs.html"> + + + <b>2.1.</b> + + List of generic Hivemall functions + + </a> + + + + </li> + + <li class="chapter " data-level="2.2" data-path="../misc/topk.html"> + + <a href="../misc/topk.html"> + + + <b>2.2.</b> + + Efficient Top-K query processing + + </a> + + + + </li> + + <li class="chapter " data-level="2.3" data-path="../misc/tokenizer.html"> + + <a href="../misc/tokenizer.html"> + + + <b>2.3.</b> + + English/Japanese Text Tokenizer + + </a> + + + + </li> + + + + + <li class="header">Part III - Feature Engineering</li> + + + + <li class="chapter " data-level="3.1" data-path="scaling.html"> + + <a href="scaling.html"> + + + <b>3.1.</b> + + Feature Scaling + + </a> + + + + </li> + + <li class="chapter " data-level="3.2" data-path="hashing.html"> + + <a href="hashing.html"> + + + <b>3.2.</b> + + Feature Hashing + + </a> + + + + </li> + + <li class="chapter active" data-level="3.3" data-path="selection.html"> + + <a href="selection.html"> + + + <b>3.3.</b> + + Feature Selection + + </a> + + + + </li> + + <li class="chapter " data-level="3.4" data-path="binning.html"> + + <a href="binning.html"> + + + <b>3.4.</b> + + Feature Binning + + </a> + + + + </li> + + <li class="chapter " data-level="3.5" data-path="tfidf.html"> + + <a href="tfidf.html"> + + + <b>3.5.</b> + + TF-IDF Calculation + + </a> + + + + </li> + + <li class="chapter " data-level="3.6" data-path="ft_trans.html"> + + <a href="ft_trans.html"> + + + <b>3.6.</b> + + FEATURE TRANSFORMATION + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="3.6.1" data-path="vectorization.html"> + + <a href="vectorization.html"> + + + <b>3.6.1.</b> + + Feature Vectorization + + </a> + + + + </li> + + <li class="chapter " data-level="3.6.2" data-path="quantify.html"> + + <a href="quantify.html"> + + + <b>3.6.2.</b> + + Quantify non-number features + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part IV - Evaluation</li> + + + + <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html"> + + <a href="../eval/stat_eval.html"> + + + <b>4.1.</b> + + Statistical evaluation of a prediction model + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="4.1.1" data-path="../eval/auc.html"> + + <a href="../eval/auc.html"> + + + <b>4.1.1.</b> + + Area Under the ROC Curve + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="4.2" data-path="../eval/rank.html"> + + <a href="../eval/rank.html"> + + + <b>4.2.</b> + + Ranking Measures + + </a> + + + + </li> + + <li class="chapter " data-level="4.3" data-path="../eval/datagen.html"> + + <a href="../eval/datagen.html"> + + + <b>4.3.</b> + + Data Generation + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html"> + + <a href="../eval/lr_datagen.html"> + + + <b>4.3.1.</b> + + Logistic Regression data generation + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part V - Binary classification</li> + + + + <li class="chapter " data-level="5.1" data-path="../binaryclass/a9a.html"> + + <a href="../binaryclass/a9a.html"> + + + <b>5.1.</b> + + a9a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.1.1" data-path="../binaryclass/a9a_dataset.html"> + + <a href="../binaryclass/a9a_dataset.html"> + + + <b>5.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.2" data-path="../binaryclass/a9a_lr.html"> + + <a href="../binaryclass/a9a_lr.html"> + + + <b>5.1.2.</b> + + Logistic Regression + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.3" data-path="../binaryclass/a9a_minibatch.html"> + + <a href="../binaryclass/a9a_minibatch.html"> + + + <b>5.1.3.</b> + + Mini-batch Gradient Descent + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.2" data-path="../binaryclass/news20.html"> + + <a href="../binaryclass/news20.html"> + + + <b>5.2.</b> + + News20 Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.2.1" data-path="../binaryclass/news20_dataset.html"> + + <a href="../binaryclass/news20_dataset.html"> + + + <b>5.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.2" data-path="../binaryclass/news20_pa.html"> + + <a href="../binaryclass/news20_pa.html"> + + + <b>5.2.2.</b> + + Perceptron, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.3" data-path="../binaryclass/news20_scw.html"> + + <a href="../binaryclass/news20_scw.html"> + + + <b>5.2.3.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.4" data-path="../binaryclass/news20_adagrad.html"> + + <a href="../binaryclass/news20_adagrad.html"> + + + <b>5.2.4.</b> + + AdaGradRDA, AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.3" data-path="../binaryclass/kdd2010a.html"> + + <a href="../binaryclass/kdd2010a.html"> + + + <b>5.3.</b> + + KDD2010a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.3.1" data-path="../binaryclass/kdd2010a_dataset.html"> + + <a href="../binaryclass/kdd2010a_dataset.html"> + + + <b>5.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.3.2" data-path="../binaryclass/kdd2010a_scw.html"> + + <a href="../binaryclass/kdd2010a_scw.html"> + + + <b>5.3.2.</b> + + PA, CW, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.4" data-path="../binaryclass/kdd2010b.html"> + + <a href="../binaryclass/kdd2010b.html"> + + + <b>5.4.</b> + + KDD2010b Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.4.1" data-path="../binaryclass/kdd2010b_dataset.html"> + + <a href="../binaryclass/kdd2010b_dataset.html"> + + + <b>5.4.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.4.2" data-path="../binaryclass/kdd2010b_arow.html"> + + <a href="../binaryclass/kdd2010b_arow.html"> + + + <b>5.4.2.</b> + + AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.5" data-path="../binaryclass/webspam.html"> + + <a href="../binaryclass/webspam.html"> + + + <b>5.5.</b> + + Webspam Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.5.1" data-path="../binaryclass/webspam_dataset.html"> + + <a href="../binaryclass/webspam_dataset.html"> + + + <b>5.5.1.</b> + + Data pareparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.5.2" data-path="../binaryclass/webspam_scw.html"> + + <a href="../binaryclass/webspam_scw.html"> + + + <b>5.5.2.</b> + + PA1, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.6" data-path="../binaryclass/titanic_rf.html"> + + <a href="../binaryclass/titanic_rf.html"> + + + <b>5.6.</b> + + Kaggle Titanic Tutorial + + </a> + + + + </li> + + + + + <li class="header">Part VI - Multiclass classification</li> + + + + <li class="chapter " data-level="6.1" data-path="../multiclass/news20.html"> + + <a href="../multiclass/news20.html"> + + + <b>6.1.</b> + + News20 Multiclass Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.1.1" data-path="../multiclass/news20_dataset.html"> + + <a href="../multiclass/news20_dataset.html"> + + + <b>6.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.2" data-path="../multiclass/news20_one-vs-the-rest_dataset.html"> + + <a href="../multiclass/news20_one-vs-the-rest_dataset.html"> + + + <b>6.1.2.</b> + + Data preparation for one-vs-the-rest classifiers + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.3" data-path="../multiclass/news20_pa.html"> + + <a href="../multiclass/news20_pa.html"> + + + <b>6.1.3.</b> + + PA + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.4" data-path="../multiclass/news20_scw.html"> + + <a href="../multiclass/news20_scw.html"> + + + <b>6.1.4.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.5" data-path="../multiclass/news20_ensemble.html"> + + <a href="../multiclass/news20_ensemble.html"> + + + <b>6.1.5.</b> + + Ensemble learning + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.6" data-path="../multiclass/news20_one-vs-the-rest.html"> + + <a href="../multiclass/news20_one-vs-the-rest.html"> + + + <b>6.1.6.</b> + + one-vs-the-rest classifier + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="6.2" data-path="../multiclass/iris.html"> + + <a href="../multiclass/iris.html"> + + + <b>6.2.</b> + + Iris Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.2.1" data-path="../multiclass/iris_dataset.html"> + + <a href="../multiclass/iris_dataset.html"> + + + <b>6.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.2" data-path="../multiclass/iris_scw.html"> + + <a href="../multiclass/iris_scw.html"> + + + <b>6.2.2.</b> + + SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="../multiclass/iris_randomforest.html"> + + <a href="../multiclass/iris_randomforest.html"> + + + <b>6.2.3.</b> + + RandomForest + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VII - Regression</li> + + + + <li class="chapter " data-level="7.1" data-path="../regression/e2006.html"> + + <a href="../regression/e2006.html"> + + + <b>7.1.</b> + + E2006-tfidf regression Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.1.1" data-path="../regression/e2006_dataset.html"> + + <a href="../regression/e2006_dataset.html"> + + + <b>7.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.1.2" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>7.1.2.</b> + + Passive Aggressive, AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="7.2" data-path="../regression/kddcup12tr2.html"> + + <a href="../regression/kddcup12tr2.html"> + + + <b>7.2.</b> + + KDDCup 2012 track 2 CTR prediction Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.2.1" data-path="../regression/kddcup12tr2_dataset.html"> + + <a href="../regression/kddcup12tr2_dataset.html"> + + + <b>7.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.2" data-path="../regression/kddcup12tr2_lr.html"> + + <a href="../regression/kddcup12tr2_lr.html"> + + + <b>7.2.2.</b> + + Logistic Regression, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.3" data-path="../regression/kddcup12tr2_lr_amplify.html"> + + <a href="../regression/kddcup12tr2_lr_amplify.html"> + + + <b>7.2.3.</b> + + Logistic Regression with Amplifier + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.4" data-path="../regression/kddcup12tr2_adagrad.html"> + + <a href="../regression/kddcup12tr2_adagrad.html"> + + + <b>7.2.4.</b> + + AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VIII - Recommendation</li> + + + + <li class="chapter " data-level="8.1" data-path="../recommend/cf.html"> + + <a href="../recommend/cf.html"> + + + <b>8.1.</b> + + Collaborative Filtering + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.1.1" data-path="../recommend/item_based_cf.html"> + + <a href="../recommend/item_based_cf.html"> + + + <b>8.1.1.</b> + + Item-based Collaborative Filtering + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.2" data-path="../recommend/news20.html"> + + <a href="../recommend/news20.html"> + + + <b>8.2.</b> + + News20 related article recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.2.1" data-path="../multiclass/news20_dataset.html"> + + <a href="../multiclass/news20_dataset.html"> + + + <b>8.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.2" data-path="../recommend/news20_jaccard.html"> + + <a href="../recommend/news20_jaccard.html"> + + + <b>8.2.2.</b> + + LSH/Minhash and Jaccard Similarity + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../recommend/news20_knn.html"> + + <a href="../recommend/news20_knn.html"> + + + <b>8.2.3.</b> + + LSH/Minhash and Brute-Force Search + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.4" data-path="../recommend/news20_bbit_minhash.html"> + + <a href="../recommend/news20_bbit_minhash.html"> + + + <b>8.2.4.</b> + + kNN search using b-Bits Minhash + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.3" data-path="../recommend/movielens.html"> + + <a href="../recommend/movielens.html"> + + + <b>8.3.</b> + + MovieLens movie recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.3.1" data-path="../recommend/movielens_dataset.html"> + + <a href="../recommend/movielens_dataset.html"> + + + <b>8.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.2" data-path="../recommend/movielens_mf.html"> + + <a href="../recommend/movielens_mf.html"> + + + <b>8.3.2.</b> + + Matrix Factorization + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.3" data-path="../recommend/movielens_fm.html"> + + <a href="../recommend/movielens_fm.html"> + + + <b>8.3.3.</b> + + Factorization Machine + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.4" data-path="../recommend/movielens_cv.html"> + + <a href="../recommend/movielens_cv.html"> + + + <b>8.3.4.</b> + + 10-fold Cross Validation (Matrix Factorization) + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part IX - Anomaly Detection</li> + + + + <li class="chapter " data-level="9.1" data-path="../anomaly/lof.html"> + + <a href="../anomaly/lof.html"> + + + <b>9.1.</b> + + Outlier Detection using Local Outlier Factor (LOF) + + </a> + + + + </li> + + <li class="chapter " data-level="9.2" data-path="../anomaly/sst.html"> + + <a href="../anomaly/sst.html"> + + + <b>9.2.</b> + + Change-Point Detection using Singular Spectrum Transformation (SST) + + </a> + + + + </li> + + <li class="chapter " data-level="9.3" data-path="../anomaly/changefinder.html"> + + <a href="../anomaly/changefinder.html"> + + + <b>9.3.</b> + + ChangeFinder: Detecting Outlier and Change-Point Simultaneously + + </a> + + + + </li> + + + + + <li class="header">Part X - Clustering</li> + + + + <li class="chapter " data-level="10.1" data-path="../clustering/lda.html"> + + <a href="../clustering/lda.html"> + + + <b>10.1.</b> + + Latent Dirichlet Allocation + + </a> + + + + </li> + + <li class="chapter " data-level="10.2" data-path="../clustering/plsa.html"> + + <a href="../clustering/plsa.html"> + + + <b>10.2.</b> + + Probabilistic Latent Semantic Analysis + + </a> + + + + </li> + + + + + <li class="header">Part XI - GeoSpatial functions</li> + + + + <li class="chapter " data-level="11.1" data-path="../geospatial/latlon.html"> + + <a href="../geospatial/latlon.html"> + + + <b>11.1.</b> + + Lat/Lon functions + + </a> + + + + </li> + + + + + <li class="header">Part XII - Hivemall on Spark</li> + + + + <li class="chapter " data-level="12.1" data-path="../spark/getting_started/"> + + <a href="../spark/getting_started/"> + + + <b>12.1.</b> + + Getting Started + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="12.1.1" data-path="../spark/getting_started/installation.html"> + + <a href="../spark/getting_started/installation.html"> + + + <b>12.1.1.</b> + + Installation + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="12.2" data-path="../spark/binaryclass/"> + + <a href="../spark/binaryclass/"> + + + <b>12.2.</b> + + Binary Classification + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="12.2.1" data-path="../spark/binaryclass/a9a_df.html"> + + <a href="../spark/binaryclass/a9a_df.html"> + + + <b>12.2.1.</b> + + a9a Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="12.3" data-path="../spark/binaryclass/"> + + <a href="../spark/binaryclass/"> + + + <b>12.3.</b> + + Regression + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="12.3.1" data-path="../spark/regression/e2006_df.html"> + + <a href="../spark/regression/e2006_df.html"> + + + <b>12.3.1.</b> + + E2006-tfidf regression Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="12.4" data-path="../spark/misc/misc.html"> + + <a href="../spark/misc/misc.html"> + + + <b>12.4.</b> + + Generic features + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="12.4.1" data-path="../spark/misc/topk_join.html"> + + <a href="../spark/misc/topk_join.html"> + + + <b>12.4.1.</b> + + Top-k Join processing + + </a> + + + + </li> + + <li class="chapter " data-level="12.4.2" data-path="../spark/misc/functions.html"> + + <a href="../spark/misc/functions.html"> + + + <b>12.4.2.</b> + + Other utility functions + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part XIII - Hivemall on Docker</li> + + + + <li class="chapter " data-level="13.1" data-path="../docker/getting_started.html"> + + <a href="../docker/getting_started.html"> + + + <b>13.1.</b> + + Getting Started + + </a> + + + + </li> + + + + + <li class="header">Part XIV - External References</li> + + + + <li class="chapter " data-level="14.1" > + + <a target="_blank" href="https://github.com/maropu/hivemall-spark";> + + + <b>14.1.</b> + + Hivemall on Apache Spark + + </a> + + + + </li> + + <li class="chapter " data-level="14.2" > + + <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> + + + <b>14.2.</b> + + Hivemall on Apache Pig + + </a> + + + + </li> + + + + + <li class="divider"></li> + + <li> + <a href="https://www.gitbook.com"; target="blank" class="gitbook-link"> + Published with GitBook + </a> + </li> +</ul> + + + </nav> + + + </div> + + <div class="book-body"> + + <div class="body-inner"> + + + +<div class="book-header" role="navigation"> + + + <!-- Title --> + <h1> + <i class="fa fa-circle-o-notch fa-spin"></i> + <a href=".." >Feature Selection</a> + </h1> +</div> + + + + + <div class="page-wrapper" tabindex="-1" role="main"> + <div class="page-inner"> + +<div id="book-search-results"> + <div class="search-noresults"> + + <section class="normal markdown-section"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + 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><a href="https://en.wikipedia.org/wiki/Feature_selection"; target="_blank">Feature Selection</a> is the process of selecting a subset of relevant features for use in model construction. </p> +<p>It is a useful technique to 1) improve prediction results by omitting redundant features, 2) to shorten training time, and 3) to know important features for prediction.</p> +<p><em>Note: This feature is supported from Hivemall v0.5-rc.1 or later.</em></p> +<!-- toc --><div id="toc" class="toc"> + +<ul> +<li><a href="#supported-feature-selection-algorithms">Supported Feature Selection algorithms</a></li> +<li><a href="#usage">Usage</a><ul> +<li><a href="#feature-selection-based-on-chi-square-test">Feature Selection based on Chi-square test</a></li> +<li><a href="#feature-selection-based-on-signal-noise-ratio-snr">Feature Selection based on Signal Noise Ratio (SNR)</a></li> +</ul> +</li> +<li><a href="#function-signatures">Function signatures</a><ul> +<li><a href="#udaf-transposeanddotxarraynumber-yarraynumberarrayarraydouble">[UDAF] <code>transpose_and_dot(X::array<number>, Y::array<number>)::array<array<double>></code></a></li> +<li><a href="#udf-selectkbestxarraynumber-importancelistarraynumber-kintarraydouble">[UDF] <code>select_k_best(X::array<number>, importance_list::array<number>, k::int)::array<double></code></a></li> +<li><a href="#udf-chi2observedarrayarraynumber-expectedarrayarraynumberstructarraydouble-arraydouble">[UDF] <code>chi2(observed::array<array<number>>, expected::array<array<number>>)::struct<array<double>, array<double>></code></a></li> +<li><a href="#udaf-snrxarraynumber-yarrayintarraydouble">[UDAF] <code>snr(X::array<number>, Y::array<int>)::array<double></code></a></li> +</ul> +</li> +</ul> + +</div><!-- tocstop --> +<h1 id="supported-feature-selection-algorithms">Supported Feature Selection algorithms</h1> +<ul> +<li>Chi-square (Chi2)<ul> +<li>In statistics, the <span class="katex"><span class="katex-mathml"><math><semantics><mrow><msup><mi>χ</mi><mn>2</mn></msup></mrow><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.8141079999999999em;"></span><span class="strut bottom" style="height:1.008548em;vertical-align:-0.19444em;"></span><span class="base textstyle uncramped"><span class="mord"><span class="mord mathit">χ</span><span class="vlist"><span style="top:-0.363em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle uncramped"><span class="mord mathrm">2</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span></span></span></span> test is applied to test the indep endence of two even events. Chi-square statistics between every feature variable and the target variable can be applied to Feature Selection. Refer <a href="http://nlp.stanford.edu/IR-book/html/htmledition/feature-selectionchi2-feature-selection-1.html"; target="_blank">this article</a> for Mathematical details.</li> +</ul> +</li> +<li>Signal Noise Ratio (SNR)<ul> +<li>The Signal Noise Ratio (SNR) is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. SNR is defined as <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi mathvariant="normal">∣</mi><msub><mi>μ</mi><mrow><mn>1</mn></mrow></msub><mo>−</mo><msub><mi>μ</mi><mrow><mn>2</mn></mrow></msub><mi mathvariant="normal">∣</mi><mi mathvariant="normal">/</mi><mo>(</mo><msub><mi>σ</mi><mrow><mn>1</mn></mrow></msub><mo>+</mo><msub><mi>σ</mi><mrow><mn>2</mn></mrow></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">|\mu_{1} - \mu_{2}| / (\sigma_{1} + \sigma_{2})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mord mathrm">∣</span><s pan class="mord"><span class="mord mathit">μ</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathrm">1</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span><span class="mbin">−</span><span class="mord"><span class="mord mathit">μ</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathrm">2</span></span></span></span><span class="baseline-fix"><span class= "fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span><span class="mord mathrm">∣</span><span class="mord mathrm">/</span><span class="mopen">(</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">σ</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathrm">1</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span><span class="mbin">+</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">σ</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0. 03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathrm">2</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span><span class="mclose">)</span></span></span></span>, where <span class="katex"><span class="katex-mathml"><math><semantics><mrow><msub><mi>μ</mi><mrow><mi>k</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\mu_{k}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.43056em;"></span><span class="strut bottom" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="base textstyle uncramped"><span class="mord"><span class="mord mathit">μ</span><span class="vlist"><span style="t op:0.15em;margin-right:0.05em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathit" style="margin-right:0.03148em;">k</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span>​</span></span></span></span></span></span> is the mean value of the variable in classes <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>k</mi></mrow><annotation encoding="application/x-tex">k</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.69444em;"></span><span class="strut bottom" style="height:0.69444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord mathit" style="margin-right:0.03148em;">k</span>< /span></span></span>, and <span class="katex"><span class="katex-mathml"><math><semantics><mrow><msub><mi>σ</mi><mrow><mi>k</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\sigma_{k}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.43056em;"></span><span class="strut bottom" style="height:0.58056em;vertical-align:-0.15em;"></span><span class="base textstyle uncramped"><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">σ</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">​</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathit" style="margin-right:0.03148em;">k</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5">< span style="font-size:0em;">​</span></span>​</span></span></span></span></span></span> is the standard deviations of the variable in classes <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>k</mi></mrow><annotation encoding="application/x-tex">k</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.69444em;"></span><span class="strut bottom" style="height:0.69444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord mathit" style="margin-right:0.03148em;">k</span></span></span></span>. Clearly, features with larger SNR are useful for classification.</li> +</ul> +</li> +</ul> +<h1 id="usage">Usage</h1> +<h2 id="feature-selection-based-on-chi-square-test">Feature Selection based on Chi-square test</h2> +<pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">input</span> ( + X <span class="hljs-built_in">array</span><<span class="hljs-keyword">double</span>>, <span class="hljs-comment">-- features</span> + Y <span class="hljs-built_in">array</span><<span class="hljs-built_in">int</span>> <span class="hljs-comment">-- binarized label</span> +); + +<span class="hljs-keyword">set</span> hivevar:k=<span class="hljs-number">2</span>; + +WITH stats AS ( + <span class="hljs-keyword">SELECT</span> + transpose_and_dot(Y, X) <span class="hljs-keyword">AS</span> observed, <span class="hljs-comment">-- array<array<double>>, shape = (n_classes, n_features)</span> + array_sum(X) <span class="hljs-keyword">AS</span> feature_count, <span class="hljs-comment">-- n_features col vector, shape = (1, array<double>)</span> + array_avg(Y) <span class="hljs-keyword">AS</span> class_prob <span class="hljs-comment">-- n_class col vector, shape = (1, array<double>)</span> + <span class="hljs-keyword">FROM</span> + <span class="hljs-keyword">input</span> +), +<span class="hljs-keyword">test</span> <span class="hljs-keyword">AS</span> ( + <span class="hljs-keyword">SELECT</span> + transpose_and_dot(class_prob, feature_count) <span class="hljs-keyword">AS</span> expected <span class="hljs-comment">-- array<array<double>>, shape = (n_class, n_features)</span> + <span class="hljs-keyword">FROM</span> + stats +), +chi2 <span class="hljs-keyword">AS</span> ( + <span class="hljs-keyword">SELECT</span> + chi2(r.observed, l.expected) <span class="hljs-keyword">AS</span> v <span class="hljs-comment">-- struct<array<double>, array<double>>, each shape = (1, n_features)</span> + <span class="hljs-keyword">FROM</span> + <span class="hljs-keyword">test</span> l + <span class="hljs-keyword">CROSS</span> <span class="hljs-keyword">JOIN</span> stats r +) +<span class="hljs-keyword">SELECT</span> + select_k_best(l.X, r.v.chi2, ${k}) <span class="hljs-keyword">as</span> features <span class="hljs-comment">-- top-k feature selection based on chi2 score</span> +<span class="hljs-keyword">FROM</span> + <span class="hljs-keyword">input</span> l + <span class="hljs-keyword">CROSS</span> <span class="hljs-keyword">JOIN</span> chi2 r; +</code></pre> +<h2 id="feature-selection-based-on-signal-noise-ratio-snr">Feature Selection based on Signal Noise Ratio (SNR)</h2> +<pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> <span class="hljs-keyword">input</span> ( + X <span class="hljs-built_in">array</span><<span class="hljs-keyword">double</span>>, <span class="hljs-comment">-- features</span> + Y <span class="hljs-built_in">array</span><<span class="hljs-built_in">int</span>> <span class="hljs-comment">-- binarized label</span> +); + +<span class="hljs-keyword">set</span> hivevar:k=<span class="hljs-number">2</span>; + +WITH snr AS ( + <span class="hljs-keyword">SELECT</span> snr(X, Y) <span class="hljs-keyword">AS</span> snr <span class="hljs-comment">-- aggregated SNR as array<double>, shape = (1, #features)</span> + <span class="hljs-keyword">FROM</span> <span class="hljs-keyword">input</span> +) +<span class="hljs-keyword">SELECT</span> + select_k_best(X, snr, ${k}) <span class="hljs-keyword">as</span> features +<span class="hljs-keyword">FROM</span> + <span class="hljs-keyword">input</span> + <span class="hljs-keyword">CROSS</span> <span class="hljs-keyword">JOIN</span> snr; +</code></pre> +<h1 id="function-signatures">Function signatures</h1> +<h3 id="udaf-transposeanddotxarraynumber-yarraynumberarrayarraydouble">[UDAF] <code>transpose_and_dot(X::array<number>, Y::array<number>)::array<array<double>></code></h3> +<h5 id="input">Input</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<number></code> X</th> +<th style="text-align:center"><code>array<number></code> Y</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">a row of matrix</td> +<td style="text-align:center">a row of matrix</td> +</tr> +</tbody> +</table> +<h5 id="output">Output</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<array<double>></code> dot product</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center"><code>dot(X.T, Y)</code> of shape = (X.#cols, Y.#cols)</td> +</tr> +</tbody> +</table> +<h3 id="udf-selectkbestxarraynumber-importancelistarraynumber-kintarraydouble">[UDF] <code>select_k_best(X::array<number>, importance_list::array<number>, k::int)::array<double></code></h3> +<h5 id="input">Input</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<number></code> X</th> +<th style="text-align:center"><code>array<number></code> importance_list</th> +<th style="text-align:center"><code>int</code> k</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">feature vector</td> +<td style="text-align:center">importance of each feature</td> +<td style="text-align:center">the number of features to be selected</td> +</tr> +</tbody> +</table> +<h5 id="output">Output</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<array<double>></code> k-best features</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">top-k elements from feature vector <code>X</code> based on importance list</td> +</tr> +</tbody> +</table> +<h3 id="udf-chi2observedarrayarraynumber-expectedarrayarraynumberstructarraydouble-arraydouble">[UDF] <code>chi2(observed::array<array<number>>, expected::array<array<number>>)::struct<array<double>, array<double>></code></h3> +<h5 id="input">Input</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<number></code> observed</th> +<th style="text-align:center"><code>array<number></code> expected</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">observed features</td> +<td style="text-align:center">expected features <code>dot(class_prob.T, feature_count)</code></td> +</tr> +</tbody> +</table> +<p>Both of <code>observed</code> and <code>expected</code> have a shape <code>(#classes, #features)</code></p> +<h5 id="output">Output</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>struct<array<double>, array<double>></code> importance_list</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">chi2-value and p-value for each feature</td> +</tr> +</tbody> +</table> +<h3 id="udaf-snrxarraynumber-yarrayintarraydouble">[UDAF] <code>snr(X::array<number>, Y::array<int>)::array<double></code></h3> +<h5 id="input">Input</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<number></code> X</th> +<th style="text-align:center"><code>array<int></code> Y</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">feature vector</td> +<td style="text-align:center">one hot label</td> +</tr> +</tbody> +</table> +<h5 id="output">Output</h5> +<table> +<thead> +<tr> +<th style="text-align:center"><code>array<double></code> importance_list</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">Signal Noise Ratio for each feature</td> +</tr> +</tbody> +</table> +<p><div id="page-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. +</font></sub></p> +</div></p> + + + </section> + + </div> + <div class="search-results"> + <div class="has-results"> + + <h1 class="search-results-title"><span class='search-results-count'></span> results matching "<span class='search-query'></span>"</h1> + <ul class="search-results-list"></ul> + + </div> + <div class="no-results"> + + <h1 class="search-results-title">No results matching "<span class='search-query'></span>"</h1> + + </div> + </div> +</div> + + </div> + </div> + + </div> + + + + + </div> + + <script> + var gitbook = gitbook || []; + gitbook.push(function() { + gitbook.page.hasChanged({"page":{"title":"Feature Selection","level":"3.3","depth":1,"next":{"title":"Feature Binning","level":"3.4","depth":1,"path":"ft_engineering/binning.md","ref":"ft_engineering/binning.md","articles":[]},"previous":{"title":"Feature Hashing","level":"3.2","depth":1,"path":"ft_engineering/hashing.md","ref":"ft_engineering/hashing.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search"; :{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"http://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"ft_engineering/selection.md","mtime":"2017-05-08T08:38:35.000Z","type":"markdown"},"gitbook":{"version":"3.2.2","time":"2017-05-08T08:42:42.226Z"},"basePath":"..","book":{"language":""}}); + }); + </script> +</div> + + + <script src="../gitbook/gitbook.js"></script> + <script src="../gitbook/theme.js"></script> + + + <script src="../gitbook/gitbook-plugin-edit-link/plugin.js"></script> + + + + <script src="../gitbook/gitbook-plugin-github/plugin.js"></script> + + + + <script src="../gitbook/gitbook-plugin-splitter/splitter.js"></script> + + + + <script src="../gitbook/gitbook-plugin-etoc/plugin.js"></script> + + + + <script src="../gitbook/gitbook-plugin-toggle-chapters/toggle.js"></script> + + + + <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js";></script> + + + + <script src="../gitbook/gitbook-plugin-anchorjs/anchor-style.js"></script> + + + + <script src="../gitbook/gitbook-plugin-expandable-chapters/expandable-chapters.js"></script> + + + + <script src="../gitbook/gitbook-plugin-search/search-engine.js"></script> + + + + <script src="../gitbook/gitbook-plugin-search/search.js"></script> + + + + <script src="../gitbook/gitbook-plugin-lunr/lunr.min.js"></script> + + + + <script src="../gitbook/gitbook-plugin-lunr/search-lunr.js"></script> + + + + <script src="../gitbook/gitbook-plugin-sharing/buttons.js"></script> + + + + <script src="../gitbook/gitbook-plugin-fontsettings/fontsettings.js"></script> + + + + <script src="../gitbook/gitbook-plugin-theme-api/theme-api.js"></script> + + + + </body> +</html> +
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/15b9991a/userguide/ft_engineering/tfidf.html ---------------------------------------------------------------------- diff --git a/userguide/ft_engineering/tfidf.html b/userguide/ft_engineering/tfidf.html index b9cf591..d752be1 100644 --- a/userguide/ft_engineering/tfidf.html +++ b/userguide/ft_engineering/tfidf.html @@ -4,7 +4,7 @@ <head> <meta charset="UTF-8"> <meta content="text/html; charset=utf-8" http-equiv="Content-Type"> - <title>TF-IDF calculation · Hivemall User Manual</title> + <title>TF-IDF Calculation · Hivemall User Manual</title> <meta http-equiv="X-UA-Compatible" content="IE=edge" /> <meta name="description" content=""> <meta name="generator" content="GitBook 3.2.2"> @@ -100,7 +100,7 @@ <link rel="next" href="ft_trans.html" /> - <link rel="prev" href="hashing.html" /> + <link rel="prev" href="binning.html" /> </head> @@ -568,14 +568,14 @@ </li> - <li class="chapter active" data-level="3.3" data-path="tfidf.html"> + <li class="chapter " data-level="3.3" data-path="selection.html"> - <a href="tfidf.html"> + <a href="selection.html"> <b>3.3.</b> - TF-IDF calculation + Feature Selection </a> @@ -583,30 +583,29 @@ </li> - <li class="chapter " data-level="3.4" data-path="ft_trans.html"> + <li class="chapter " data-level="3.4" data-path="binning.html"> - <a href="ft_trans.html"> + <a href="binning.html"> <b>3.4.</b> - FEATURE TRANSFORMATION + Feature Binning </a> - <ul class="articles"> - + </li> - <li class="chapter " data-level="3.4.1" data-path="vectorizer.html"> + <li class="chapter active" data-level="3.5" data-path="tfidf.html"> - <a href="vectorizer.html"> + <a href="tfidf.html"> - <b>3.4.1.</b> + <b>3.5.</b> - Vectorize Features + TF-IDF Calculation </a> @@ -614,34 +613,45 @@ </li> - <li class="chapter " data-level="3.4.2" data-path="quantify.html"> + <li class="chapter " data-level="3.6" data-path="ft_trans.html"> - <a href="quantify.html"> + <a href="ft_trans.html"> - <b>3.4.2.</b> + <b>3.6.</b> - Quantify non-number features + FEATURE TRANSFORMATION </a> - </li> + <ul class="articles"> + + <li class="chapter " data-level="3.6.1" data-path="vectorization.html"> + + <a href="vectorization.html"> + + + <b>3.6.1.</b> + + Feature Vectorization + + </a> + - </ul> </li> - <li class="chapter " data-level="3.5" data-path="feature_selection.html"> + <li class="chapter " data-level="3.6.2" data-path="quantify.html"> - <a href="feature_selection.html"> + <a href="quantify.html"> - <b>3.5.</b> + <b>3.6.2.</b> - Feature selection + Quantify non-number features </a> @@ -650,6 +660,11 @@ </li> + </ul> + + </li> + + <li class="header">Part IV - Evaluation</li> @@ -1959,7 +1974,7 @@ <!-- Title --> <h1> <i class="fa fa-circle-o-notch fa-spin"></i> - <a href=".." >TF-IDF calculation</a> + <a href=".." >TF-IDF Calculation</a> </h1> </div> @@ -2177,7 +2192,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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