Github user takuti commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/107#discussion_r134138339
--- Diff: docs/gitbook/eval/binary_classification_measures.md ---
@@ -0,0 +1,232 @@
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+<!-- toc -->
+
+# Binary problems
+
+Binary classification problem is the task to predict the label of each
data given two categorized dataset.
+
+Hivemall provides some tutorials to deal with binary classification
problems as follows:
+
+- [Online advertisement click prediction](../binaryclass/general.html)
+- [News classification](../binaryclass/news20_dataset.html)
+
+This page focuses on the evaluation of the results from such binary
classification problems.
+If you want to know about Area Under the ROC Curve, please check
[AUC](./auc.md) page.
+
+# Example
+
+For the metrics explanation, this page introduces toy example data and two
metrics.
+
+## Data
+
+The following table shows the sample of binary classification's prediction.
+In this case, `1` means positive label and `0` means negative label.
+Left column includes supervised label data,
+Right column includes are predicted label by a binary classifier.w
+
+| truth label| predicted label |
+|:---:|:---:|
+| 1 | 0 |
+| 0 | 1 |
+| 0 | 0 |
+| 1 | 1 |
+| 0 | 1 |
+| 0 | 0 |
+
+## Preliminary metrics
+
+Some evaluation metrics are calculated based on 4 values:
+
+- True Positive: truth label is positive and predicted label is also
positive
+- True Negative: truth label is negative and predicted label is also
negative
+- False Positive: truth label is negative but predicted label is positive
+- False Negative: truth label is positive but predicted label is negative
+
+In this example, we can obtain those values:
+
+- True Positive: 1
--- End diff --
Values (or table) are incorrect; the above table should be TP=1, TN=2,
FN=1, FP=2
Since TP, FP, FN and TN are complicated for beginners, it's better to show
which row corresponds to each of them as:
| truth label| predicted label | |
|:---:|:---:|:---|
| 1 | 0 |False Negative|
| 0 | 1 |False Positive|
| 0 | 0 |True Negative|
| 1 | 1 |True Positive|
| 0 | 1 |False Positive|
| 0 | 0 |True Negative|
In addition, I would recommend you to clearly describe "TP and TN represent
**correct** classification, and FP and FN illustrate **incorrect** ones."
Furthermore, adding a link to external page which explains TP, FP, TN, FN
somewhere like Wikipedia would be better.
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