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Posted to commits@hivemall.apache.org by my...@apache.org on 2017/06/23 10:04:02 UTC
[27/41] incubator-hivemall-site git commit: Added descriptions about
Feature Pairing in the user guide
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ba518dab/userguide/ft_engineering/scaling.html
----------------------------------------------------------------------
diff --git a/userguide/ft_engineering/scaling.html b/userguide/ft_engineering/scaling.html
index 9c9b672..5d5bdfa 100644
--- a/userguide/ft_engineering/scaling.html
+++ b/userguide/ft_engineering/scaling.html
@@ -598,14 +598,30 @@
</li>
- <li class="chapter " data-level="3.5" data-path="tfidf.html">
+ <li class="chapter " data-level="3.5" data-path="pairing.html">
- <a href="tfidf.html">
+ <a href="pairing.html">
<b>3.5.</b>
- TF-IDF Calculation
+ FEATURE PAIRING
+
+ </a>
+
+
+
+ <ul class="articles">
+
+
+ <li class="chapter " data-level="3.5.1" data-path="polynomial.html">
+
+ <a href="polynomial.html">
+
+
+ <b>3.5.1.</b>
+
+ Polynomial Features
</a>
@@ -613,6 +629,11 @@
</li>
+
+ </ul>
+
+ </li>
+
<li class="chapter " data-level="3.6" data-path="ft_trans.html">
<a href="ft_trans.html">
@@ -664,6 +685,21 @@
</li>
+ <li class="chapter " data-level="3.7" data-path="tfidf.html">
+
+ <a href="tfidf.html">
+
+
+ <b>3.7.</b>
+
+ TF-IDF Calculation
+
+ </a>
+
+
+
+ </li>
+
@@ -761,7 +797,7 @@
- <li class="header">Part V - Prediction</li>
+ <li class="header">Part V - Supervised Learning</li>
@@ -780,27 +816,19 @@
</li>
- <li class="chapter " data-level="5.2" data-path="../regression/general.html">
-
- <a href="../regression/general.html">
-
-
- <b>5.2.</b>
-
- Regression
-
- </a>
-
-
- </li>
- <li class="chapter " data-level="5.3" data-path="../binaryclass/general.html">
+
+ <li class="header">Part VI - Binary classification</li>
+
+
+
+ <li class="chapter " data-level="6.1" data-path="../binaryclass/general.html">
<a href="../binaryclass/general.html">
- <b>5.3.</b>
+ <b>6.1.</b>
Binary Classification
@@ -810,21 +838,14 @@
</li>
-
-
-
- <li class="header">Part VI - Binary classification tutorials</li>
-
-
-
- <li class="chapter " data-level="6.1" data-path="../binaryclass/a9a.html">
+ <li class="chapter " data-level="6.2" data-path="../binaryclass/a9a.html">
<a href="../binaryclass/a9a.html">
- <b>6.1.</b>
+ <b>6.2.</b>
- a9a
+ a9a tutorial
</a>
@@ -833,12 +854,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.1.1" data-path="../binaryclass/a9a_dataset.html">
+ <li class="chapter " data-level="6.2.1" data-path="../binaryclass/a9a_dataset.html">
<a href="../binaryclass/a9a_dataset.html">
- <b>6.1.1.</b>
+ <b>6.2.1.</b>
Data preparation
@@ -848,12 +869,12 @@
</li>
- <li class="chapter " data-level="6.1.2" data-path="../binaryclass/a9a_lr.html">
+ <li class="chapter " data-level="6.2.2" data-path="../binaryclass/a9a_lr.html">
<a href="../binaryclass/a9a_lr.html">
- <b>6.1.2.</b>
+ <b>6.2.2.</b>
Logistic Regression
@@ -863,12 +884,12 @@
</li>
- <li class="chapter " data-level="6.1.3" data-path="../binaryclass/a9a_minibatch.html">
+ <li class="chapter " data-level="6.2.3" data-path="../binaryclass/a9a_minibatch.html">
<a href="../binaryclass/a9a_minibatch.html">
- <b>6.1.3.</b>
+ <b>6.2.3.</b>
Mini-batch Gradient Descent
@@ -883,14 +904,14 @@
</li>
- <li class="chapter " data-level="6.2" data-path="../binaryclass/news20.html">
+ <li class="chapter " data-level="6.3" data-path="../binaryclass/news20.html">
<a href="../binaryclass/news20.html">
- <b>6.2.</b>
+ <b>6.3.</b>
- News20
+ News20 tutorial
</a>
@@ -899,12 +920,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.2.1" data-path="../binaryclass/news20_dataset.html">
+ <li class="chapter " data-level="6.3.1" data-path="../binaryclass/news20_dataset.html">
<a href="../binaryclass/news20_dataset.html">
- <b>6.2.1.</b>
+ <b>6.3.1.</b>
Data preparation
@@ -914,12 +935,12 @@
</li>
- <li class="chapter " data-level="6.2.2" data-path="../binaryclass/news20_pa.html">
+ <li class="chapter " data-level="6.3.2" data-path="../binaryclass/news20_pa.html">
<a href="../binaryclass/news20_pa.html">
- <b>6.2.2.</b>
+ <b>6.3.2.</b>
Perceptron, Passive Aggressive
@@ -929,12 +950,12 @@
</li>
- <li class="chapter " data-level="6.2.3" data-path="../binaryclass/news20_scw.html">
+ <li class="chapter " data-level="6.3.3" data-path="../binaryclass/news20_scw.html">
<a href="../binaryclass/news20_scw.html">
- <b>6.2.3.</b>
+ <b>6.3.3.</b>
CW, AROW, SCW
@@ -944,12 +965,12 @@
</li>
- <li class="chapter " data-level="6.2.4" data-path="../binaryclass/news20_adagrad.html">
+ <li class="chapter " data-level="6.3.4" data-path="../binaryclass/news20_adagrad.html">
<a href="../binaryclass/news20_adagrad.html">
- <b>6.2.4.</b>
+ <b>6.3.4.</b>
AdaGradRDA, AdaGrad, AdaDelta
@@ -964,14 +985,14 @@
</li>
- <li class="chapter " data-level="6.3" data-path="../binaryclass/kdd2010a.html">
+ <li class="chapter " data-level="6.4" data-path="../binaryclass/kdd2010a.html">
<a href="../binaryclass/kdd2010a.html">
- <b>6.3.</b>
+ <b>6.4.</b>
- KDD2010a
+ KDD2010a tutorial
</a>
@@ -980,12 +1001,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.3.1" data-path="../binaryclass/kdd2010a_dataset.html">
+ <li class="chapter " data-level="6.4.1" data-path="../binaryclass/kdd2010a_dataset.html">
<a href="../binaryclass/kdd2010a_dataset.html">
- <b>6.3.1.</b>
+ <b>6.4.1.</b>
Data preparation
@@ -995,12 +1016,12 @@
</li>
- <li class="chapter " data-level="6.3.2" data-path="../binaryclass/kdd2010a_scw.html">
+ <li class="chapter " data-level="6.4.2" data-path="../binaryclass/kdd2010a_scw.html">
<a href="../binaryclass/kdd2010a_scw.html">
- <b>6.3.2.</b>
+ <b>6.4.2.</b>
PA, CW, AROW, SCW
@@ -1015,14 +1036,14 @@
</li>
- <li class="chapter " data-level="6.4" data-path="../binaryclass/kdd2010b.html">
+ <li class="chapter " data-level="6.5" data-path="../binaryclass/kdd2010b.html">
<a href="../binaryclass/kdd2010b.html">
- <b>6.4.</b>
+ <b>6.5.</b>
- KDD2010b
+ KDD2010b tutorial
</a>
@@ -1031,12 +1052,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.4.1" data-path="../binaryclass/kdd2010b_dataset.html">
+ <li class="chapter " data-level="6.5.1" data-path="../binaryclass/kdd2010b_dataset.html">
<a href="../binaryclass/kdd2010b_dataset.html">
- <b>6.4.1.</b>
+ <b>6.5.1.</b>
Data preparation
@@ -1046,12 +1067,12 @@
</li>
- <li class="chapter " data-level="6.4.2" data-path="../binaryclass/kdd2010b_arow.html">
+ <li class="chapter " data-level="6.5.2" data-path="../binaryclass/kdd2010b_arow.html">
<a href="../binaryclass/kdd2010b_arow.html">
- <b>6.4.2.</b>
+ <b>6.5.2.</b>
AROW
@@ -1066,14 +1087,14 @@
</li>
- <li class="chapter " data-level="6.5" data-path="../binaryclass/webspam.html">
+ <li class="chapter " data-level="6.6" data-path="../binaryclass/webspam.html">
<a href="../binaryclass/webspam.html">
- <b>6.5.</b>
+ <b>6.6.</b>
- Webspam
+ Webspam tutorial
</a>
@@ -1082,12 +1103,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.5.1" data-path="../binaryclass/webspam_dataset.html">
+ <li class="chapter " data-level="6.6.1" data-path="../binaryclass/webspam_dataset.html">
<a href="../binaryclass/webspam_dataset.html">
- <b>6.5.1.</b>
+ <b>6.6.1.</b>
Data pareparation
@@ -1097,12 +1118,12 @@
</li>
- <li class="chapter " data-level="6.5.2" data-path="../binaryclass/webspam_scw.html">
+ <li class="chapter " data-level="6.6.2" data-path="../binaryclass/webspam_scw.html">
<a href="../binaryclass/webspam_scw.html">
- <b>6.5.2.</b>
+ <b>6.6.2.</b>
PA1, AROW, SCW
@@ -1117,14 +1138,14 @@
</li>
- <li class="chapter " data-level="6.6" data-path="../binaryclass/titanic_rf.html">
+ <li class="chapter " data-level="6.7" data-path="../binaryclass/titanic_rf.html">
<a href="../binaryclass/titanic_rf.html">
- <b>6.6.</b>
+ <b>6.7.</b>
- Kaggle Titanic
+ Kaggle Titanic tutorial
</a>
@@ -1135,7 +1156,7 @@
- <li class="header">Part VII - Multiclass classification tutorials</li>
+ <li class="header">Part VII - Multiclass classification</li>
@@ -1146,7 +1167,7 @@
<b>7.1.</b>
- News20 Multiclass
+ News20 Multiclass tutorial
</a>
@@ -1257,7 +1278,7 @@
<b>7.2.</b>
- Iris
+ Iris tutorial
</a>
@@ -1319,18 +1340,33 @@
- <li class="header">Part VIII - Regression tutorials</li>
+ <li class="header">Part VIII - Regression</li>
- <li class="chapter " data-level="8.1" data-path="../regression/e2006.html">
+ <li class="chapter " data-level="8.1" data-path="../regression/general.html">
- <a href="../regression/e2006.html">
+ <a href="../regression/general.html">
<b>8.1.</b>
- E2006-tfidf regression
+ 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>
@@ -1339,12 +1375,12 @@
<ul class="articles">
- <li class="chapter " data-level="8.1.1" data-path="../regression/e2006_dataset.html">
+ <li class="chapter " data-level="8.2.1" data-path="../regression/e2006_dataset.html">
<a href="../regression/e2006_dataset.html">
- <b>8.1.1.</b>
+ <b>8.2.1.</b>
Data preparation
@@ -1354,12 +1390,12 @@
</li>
- <li class="chapter " data-level="8.1.2" data-path="../regression/e2006_arow.html">
+ <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html">
<a href="../regression/e2006_arow.html">
- <b>8.1.2.</b>
+ <b>8.2.2.</b>
Passive Aggressive, AROW
@@ -1374,14 +1410,14 @@
</li>
- <li class="chapter " data-level="8.2" data-path="../regression/kddcup12tr2.html">
+ <li class="chapter " data-level="8.3" data-path="../regression/kddcup12tr2.html">
<a href="../regression/kddcup12tr2.html">
- <b>8.2.</b>
+ <b>8.3.</b>
- KDDCup 2012 track 2 CTR prediction
+ KDDCup 2012 track 2 CTR prediction tutorial
</a>
@@ -1390,12 +1426,12 @@
<ul class="articles">
- <li class="chapter " data-level="8.2.1" data-path="../regression/kddcup12tr2_dataset.html">
+ <li class="chapter " data-level="8.3.1" data-path="../regression/kddcup12tr2_dataset.html">
<a href="../regression/kddcup12tr2_dataset.html">
- <b>8.2.1.</b>
+ <b>8.3.1.</b>
Data preparation
@@ -1405,12 +1441,12 @@
</li>
- <li class="chapter " data-level="8.2.2" data-path="../regression/kddcup12tr2_lr.html">
+ <li class="chapter " data-level="8.3.2" data-path="../regression/kddcup12tr2_lr.html">
<a href="../regression/kddcup12tr2_lr.html">
- <b>8.2.2.</b>
+ <b>8.3.2.</b>
Logistic Regression, Passive Aggressive
@@ -1420,12 +1456,12 @@
</li>
- <li class="chapter " data-level="8.2.3" data-path="../regression/kddcup12tr2_lr_amplify.html">
+ <li class="chapter " data-level="8.3.3" data-path="../regression/kddcup12tr2_lr_amplify.html">
<a href="../regression/kddcup12tr2_lr_amplify.html">
- <b>8.2.3.</b>
+ <b>8.3.3.</b>
Logistic Regression with Amplifier
@@ -1435,12 +1471,12 @@
</li>
- <li class="chapter " data-level="8.2.4" data-path="../regression/kddcup12tr2_adagrad.html">
+ <li class="chapter " data-level="8.3.4" data-path="../regression/kddcup12tr2_adagrad.html">
<a href="../regression/kddcup12tr2_adagrad.html">
- <b>8.2.4.</b>
+ <b>8.3.4.</b>
AdaGrad, AdaDelta
@@ -2291,7 +2327,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ba518dab/userguide/ft_engineering/selection.html
----------------------------------------------------------------------
diff --git a/userguide/ft_engineering/selection.html b/userguide/ft_engineering/selection.html
index f117cb4..b1e8d13 100644
--- a/userguide/ft_engineering/selection.html
+++ b/userguide/ft_engineering/selection.html
@@ -598,14 +598,30 @@
</li>
- <li class="chapter " data-level="3.5" data-path="tfidf.html">
+ <li class="chapter " data-level="3.5" data-path="pairing.html">
- <a href="tfidf.html">
+ <a href="pairing.html">
<b>3.5.</b>
- TF-IDF Calculation
+ FEATURE PAIRING
+
+ </a>
+
+
+
+ <ul class="articles">
+
+
+ <li class="chapter " data-level="3.5.1" data-path="polynomial.html">
+
+ <a href="polynomial.html">
+
+
+ <b>3.5.1.</b>
+
+ Polynomial Features
</a>
@@ -613,6 +629,11 @@
</li>
+
+ </ul>
+
+ </li>
+
<li class="chapter " data-level="3.6" data-path="ft_trans.html">
<a href="ft_trans.html">
@@ -664,6 +685,21 @@
</li>
+ <li class="chapter " data-level="3.7" data-path="tfidf.html">
+
+ <a href="tfidf.html">
+
+
+ <b>3.7.</b>
+
+ TF-IDF Calculation
+
+ </a>
+
+
+
+ </li>
+
@@ -761,7 +797,7 @@
- <li class="header">Part V - Prediction</li>
+ <li class="header">Part V - Supervised Learning</li>
@@ -780,27 +816,19 @@
</li>
- <li class="chapter " data-level="5.2" data-path="../regression/general.html">
-
- <a href="../regression/general.html">
-
-
- <b>5.2.</b>
-
- Regression
-
- </a>
-
-
- </li>
- <li class="chapter " data-level="5.3" data-path="../binaryclass/general.html">
+
+ <li class="header">Part VI - Binary classification</li>
+
+
+
+ <li class="chapter " data-level="6.1" data-path="../binaryclass/general.html">
<a href="../binaryclass/general.html">
- <b>5.3.</b>
+ <b>6.1.</b>
Binary Classification
@@ -810,21 +838,14 @@
</li>
-
-
-
- <li class="header">Part VI - Binary classification tutorials</li>
-
-
-
- <li class="chapter " data-level="6.1" data-path="../binaryclass/a9a.html">
+ <li class="chapter " data-level="6.2" data-path="../binaryclass/a9a.html">
<a href="../binaryclass/a9a.html">
- <b>6.1.</b>
+ <b>6.2.</b>
- a9a
+ a9a tutorial
</a>
@@ -833,12 +854,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.1.1" data-path="../binaryclass/a9a_dataset.html">
+ <li class="chapter " data-level="6.2.1" data-path="../binaryclass/a9a_dataset.html">
<a href="../binaryclass/a9a_dataset.html">
- <b>6.1.1.</b>
+ <b>6.2.1.</b>
Data preparation
@@ -848,12 +869,12 @@
</li>
- <li class="chapter " data-level="6.1.2" data-path="../binaryclass/a9a_lr.html">
+ <li class="chapter " data-level="6.2.2" data-path="../binaryclass/a9a_lr.html">
<a href="../binaryclass/a9a_lr.html">
- <b>6.1.2.</b>
+ <b>6.2.2.</b>
Logistic Regression
@@ -863,12 +884,12 @@
</li>
- <li class="chapter " data-level="6.1.3" data-path="../binaryclass/a9a_minibatch.html">
+ <li class="chapter " data-level="6.2.3" data-path="../binaryclass/a9a_minibatch.html">
<a href="../binaryclass/a9a_minibatch.html">
- <b>6.1.3.</b>
+ <b>6.2.3.</b>
Mini-batch Gradient Descent
@@ -883,14 +904,14 @@
</li>
- <li class="chapter " data-level="6.2" data-path="../binaryclass/news20.html">
+ <li class="chapter " data-level="6.3" data-path="../binaryclass/news20.html">
<a href="../binaryclass/news20.html">
- <b>6.2.</b>
+ <b>6.3.</b>
- News20
+ News20 tutorial
</a>
@@ -899,12 +920,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.2.1" data-path="../binaryclass/news20_dataset.html">
+ <li class="chapter " data-level="6.3.1" data-path="../binaryclass/news20_dataset.html">
<a href="../binaryclass/news20_dataset.html">
- <b>6.2.1.</b>
+ <b>6.3.1.</b>
Data preparation
@@ -914,12 +935,12 @@
</li>
- <li class="chapter " data-level="6.2.2" data-path="../binaryclass/news20_pa.html">
+ <li class="chapter " data-level="6.3.2" data-path="../binaryclass/news20_pa.html">
<a href="../binaryclass/news20_pa.html">
- <b>6.2.2.</b>
+ <b>6.3.2.</b>
Perceptron, Passive Aggressive
@@ -929,12 +950,12 @@
</li>
- <li class="chapter " data-level="6.2.3" data-path="../binaryclass/news20_scw.html">
+ <li class="chapter " data-level="6.3.3" data-path="../binaryclass/news20_scw.html">
<a href="../binaryclass/news20_scw.html">
- <b>6.2.3.</b>
+ <b>6.3.3.</b>
CW, AROW, SCW
@@ -944,12 +965,12 @@
</li>
- <li class="chapter " data-level="6.2.4" data-path="../binaryclass/news20_adagrad.html">
+ <li class="chapter " data-level="6.3.4" data-path="../binaryclass/news20_adagrad.html">
<a href="../binaryclass/news20_adagrad.html">
- <b>6.2.4.</b>
+ <b>6.3.4.</b>
AdaGradRDA, AdaGrad, AdaDelta
@@ -964,14 +985,14 @@
</li>
- <li class="chapter " data-level="6.3" data-path="../binaryclass/kdd2010a.html">
+ <li class="chapter " data-level="6.4" data-path="../binaryclass/kdd2010a.html">
<a href="../binaryclass/kdd2010a.html">
- <b>6.3.</b>
+ <b>6.4.</b>
- KDD2010a
+ KDD2010a tutorial
</a>
@@ -980,12 +1001,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.3.1" data-path="../binaryclass/kdd2010a_dataset.html">
+ <li class="chapter " data-level="6.4.1" data-path="../binaryclass/kdd2010a_dataset.html">
<a href="../binaryclass/kdd2010a_dataset.html">
- <b>6.3.1.</b>
+ <b>6.4.1.</b>
Data preparation
@@ -995,12 +1016,12 @@
</li>
- <li class="chapter " data-level="6.3.2" data-path="../binaryclass/kdd2010a_scw.html">
+ <li class="chapter " data-level="6.4.2" data-path="../binaryclass/kdd2010a_scw.html">
<a href="../binaryclass/kdd2010a_scw.html">
- <b>6.3.2.</b>
+ <b>6.4.2.</b>
PA, CW, AROW, SCW
@@ -1015,14 +1036,14 @@
</li>
- <li class="chapter " data-level="6.4" data-path="../binaryclass/kdd2010b.html">
+ <li class="chapter " data-level="6.5" data-path="../binaryclass/kdd2010b.html">
<a href="../binaryclass/kdd2010b.html">
- <b>6.4.</b>
+ <b>6.5.</b>
- KDD2010b
+ KDD2010b tutorial
</a>
@@ -1031,12 +1052,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.4.1" data-path="../binaryclass/kdd2010b_dataset.html">
+ <li class="chapter " data-level="6.5.1" data-path="../binaryclass/kdd2010b_dataset.html">
<a href="../binaryclass/kdd2010b_dataset.html">
- <b>6.4.1.</b>
+ <b>6.5.1.</b>
Data preparation
@@ -1046,12 +1067,12 @@
</li>
- <li class="chapter " data-level="6.4.2" data-path="../binaryclass/kdd2010b_arow.html">
+ <li class="chapter " data-level="6.5.2" data-path="../binaryclass/kdd2010b_arow.html">
<a href="../binaryclass/kdd2010b_arow.html">
- <b>6.4.2.</b>
+ <b>6.5.2.</b>
AROW
@@ -1066,14 +1087,14 @@
</li>
- <li class="chapter " data-level="6.5" data-path="../binaryclass/webspam.html">
+ <li class="chapter " data-level="6.6" data-path="../binaryclass/webspam.html">
<a href="../binaryclass/webspam.html">
- <b>6.5.</b>
+ <b>6.6.</b>
- Webspam
+ Webspam tutorial
</a>
@@ -1082,12 +1103,12 @@
<ul class="articles">
- <li class="chapter " data-level="6.5.1" data-path="../binaryclass/webspam_dataset.html">
+ <li class="chapter " data-level="6.6.1" data-path="../binaryclass/webspam_dataset.html">
<a href="../binaryclass/webspam_dataset.html">
- <b>6.5.1.</b>
+ <b>6.6.1.</b>
Data pareparation
@@ -1097,12 +1118,12 @@
</li>
- <li class="chapter " data-level="6.5.2" data-path="../binaryclass/webspam_scw.html">
+ <li class="chapter " data-level="6.6.2" data-path="../binaryclass/webspam_scw.html">
<a href="../binaryclass/webspam_scw.html">
- <b>6.5.2.</b>
+ <b>6.6.2.</b>
PA1, AROW, SCW
@@ -1117,14 +1138,14 @@
</li>
- <li class="chapter " data-level="6.6" data-path="../binaryclass/titanic_rf.html">
+ <li class="chapter " data-level="6.7" data-path="../binaryclass/titanic_rf.html">
<a href="../binaryclass/titanic_rf.html">
- <b>6.6.</b>
+ <b>6.7.</b>
- Kaggle Titanic
+ Kaggle Titanic tutorial
</a>
@@ -1135,7 +1156,7 @@
- <li class="header">Part VII - Multiclass classification tutorials</li>
+ <li class="header">Part VII - Multiclass classification</li>
@@ -1146,7 +1167,7 @@
<b>7.1.</b>
- News20 Multiclass
+ News20 Multiclass tutorial
</a>
@@ -1257,7 +1278,7 @@
<b>7.2.</b>
- Iris
+ Iris tutorial
</a>
@@ -1319,18 +1340,33 @@
- <li class="header">Part VIII - Regression tutorials</li>
+ <li class="header">Part VIII - Regression</li>
- <li class="chapter " data-level="8.1" data-path="../regression/e2006.html">
+ <li class="chapter " data-level="8.1" data-path="../regression/general.html">
- <a href="../regression/e2006.html">
+ <a href="../regression/general.html">
<b>8.1.</b>
- E2006-tfidf regression
+ 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>
@@ -1339,12 +1375,12 @@
<ul class="articles">
- <li class="chapter " data-level="8.1.1" data-path="../regression/e2006_dataset.html">
+ <li class="chapter " data-level="8.2.1" data-path="../regression/e2006_dataset.html">
<a href="../regression/e2006_dataset.html">
- <b>8.1.1.</b>
+ <b>8.2.1.</b>
Data preparation
@@ -1354,12 +1390,12 @@
</li>
- <li class="chapter " data-level="8.1.2" data-path="../regression/e2006_arow.html">
+ <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html">
<a href="../regression/e2006_arow.html">
- <b>8.1.2.</b>
+ <b>8.2.2.</b>
Passive Aggressive, AROW
@@ -1374,14 +1410,14 @@
</li>
- <li class="chapter " data-level="8.2" data-path="../regression/kddcup12tr2.html">
+ <li class="chapter " data-level="8.3" data-path="../regression/kddcup12tr2.html">
<a href="../regression/kddcup12tr2.html">
- <b>8.2.</b>
+ <b>8.3.</b>
- KDDCup 2012 track 2 CTR prediction
+ KDDCup 2012 track 2 CTR prediction tutorial
</a>
@@ -1390,12 +1426,12 @@
<ul class="articles">
- <li class="chapter " data-level="8.2.1" data-path="../regression/kddcup12tr2_dataset.html">
+ <li class="chapter " data-level="8.3.1" data-path="../regression/kddcup12tr2_dataset.html">
<a href="../regression/kddcup12tr2_dataset.html">
- <b>8.2.1.</b>
+ <b>8.3.1.</b>
Data preparation
@@ -1405,12 +1441,12 @@
</li>
- <li class="chapter " data-level="8.2.2" data-path="../regression/kddcup12tr2_lr.html">
+ <li class="chapter " data-level="8.3.2" data-path="../regression/kddcup12tr2_lr.html">
<a href="../regression/kddcup12tr2_lr.html">
- <b>8.2.2.</b>
+ <b>8.3.2.</b>
Logistic Regression, Passive Aggressive
@@ -1420,12 +1456,12 @@
</li>
- <li class="chapter " data-level="8.2.3" data-path="../regression/kddcup12tr2_lr_amplify.html">
+ <li class="chapter " data-level="8.3.3" data-path="../regression/kddcup12tr2_lr_amplify.html">
<a href="../regression/kddcup12tr2_lr_amplify.html">
- <b>8.2.3.</b>
+ <b>8.3.3.</b>
Logistic Regression with Amplifier
@@ -1435,12 +1471,12 @@
</li>
- <li class="chapter " data-level="8.2.4" data-path="../regression/kddcup12tr2_adagrad.html">
+ <li class="chapter " data-level="8.3.4" data-path="../regression/kddcup12tr2_adagrad.html">
<a href="../regression/kddcup12tr2_adagrad.html">
- <b>8.2.4.</b>
+ <b>8.3.4.</b>
AdaGrad, AdaDelta
@@ -2099,11 +2135,11 @@
<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="msupsub"><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 mtight"><span class="mord mathrm mtight">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></spa
n></span> test is applied to test the independence 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>
+<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="msupsub"><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 mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathrm mtight">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><span class="mbin">−</span><span class="mord"><span class="mord mathit">μ</span><span class="msupsub"><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 mtight"><span class="mord scriptstyle cramped mtight"><span cl
ass="mord mathrm mtight">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><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="msupsub"><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 mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathrm mtight">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><span class="mbin">+</span><span class="mord"><
span class="mord mathit" style="margin-right:0.03588em;">σ</span><span class="msupsub"><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 mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathrm mtight">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><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="str
ut 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="msupsub"><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 mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathit mtight" 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></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-hi
dden="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="msupsub"><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;">&#x
200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathit mtight" 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></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>
+<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>
@@ -2336,7 +2372,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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