MetaCost Save

MetaCost

This method was first proposed by P. Domingos, and I coded it with Python.

Example

Below is an example of using MetaCost to perform imbalanced learning.

 >>> from sklearn.datasets import load_iris
 >>> from sklearn.linear_model import LogisticRegression
 >>> import pandas as pd
 >>> import numpy as np
 >>> S = pd.DataFrame(load_iris().data)
 >>> S['target'] = load_iris().target
 >>> LR = LogisticRegression(solver='lbfgs', multi_class='multinomial')
 >>> C = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
 >>> model = MetaCost(S, LR, C).fit('target', 3)
 >>> model.predict_proba(load_iris().data[[2]])
 >>> model.score(S[[0, 1, 2, 3]].values, S['target'])
 
 """
 Note:: The form of the cost matrix C must be as follows
 +---------------+----------+----------+----------+
 |  actual class |          |          |          |
 +               |          |          |          |
 |   +           | y(x)=j_1 | y(x)=j_2 | y(x)=j_3 |
 |       +       |          |          |          |
 |           +   |          |          |          |
 |predicted class|          |          |          |
 +---------------+----------+----------+----------+
 |   h(x)=j_1    |    0     |    a     |     b    |
 |   h(x)=j_2    |    c     |    0     |     d    |
 |   h(x)=j_3    |    e     |    f     |     0    |
 +---------------+----------+----------+----------+
 | C = np.array([[0, a, b],[c, 0 , d],[e, f, 0]]) |
 +------------------------------------------------+
 """

Reference

P. Domingos, "MetaCost: A General Method for Making Classifiers Cost-Sensitive", Proc. Int’l Conf. Knowledge Discovery and Data Mining, pp. 155-164, 1999.