Dealing with class imbalance

A classifier is only as good as the data that's used for training. One of the most common problems we face in the real world is the quality of data. For a classifier to perform well, it needs to see equal number of points for each class. But when we collect data in the real world, it's not always possible to ensure that each class has the exact same number of data points. If one class has 10 times the number of data points of the other class, then the classifier tends to get biased towards the first class. Hence we need to make sure that we account for this imbalance algorithmically. Let's see how to do that.

Create a new Python file and import the following packages:

import sys 
 
import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.ensemble import ExtraTreesClassifier  
from sklearn import cross_validation 
from sklearn.metrics import classification_report 
 
from utilities import visualize_classifier 
...