[PYTHON] One Class SVM implementation

0 Introduction

This article is written as a personal memo when implementing One Class SVM with sklearn. The points that I understood by reading other articles and the points that I want to remember are summarized using figures.

1 Preparation

The function to read is as follows.

2 Data definition

This time, we define the following as training data, test data, and outlier data.

The graphs of X_train, X_test and X_outliers are as follows, respectively.

3 Data learning

The parameters used for training are displayed in clf.get_params

In clf.decision_function (X_test), the distance from the discrimination boundary at each distribution point is shown, and a positive value means within the classification and a negative value means outside the classification.

clf.predict (X_test) returns 1 or -1 to see if each given point is an outlier.

The following is a table of predict and decision_function to check the range of X_outliers given as outliers.

Since there are many outliers, there are many data that are -1, but there are some that are 1, and it can be seen that they are within the learning data range.

4 Result display

sample_set_1 is a set of outlier data sets that were within the learning range, and sample_set_mina1 is a set of outlier data sets that were outliers.

The two points that are OK below are the points 1.

This is a simple implementation memo.