v Handling Imbalanced Classes In Logistic Regression - Machine Learning

Handling Imbalanced Classes In Logistic Regression

Like many other learning algorithms in scikit-learn, LogisticRegression comes with a built-in method of handling imbalanced classes. If we have highly imbalanced classes and have no addressed it during preprocessing, we have the option of using the class_weight parameter to weight the classes to make certain we have a balanced mix of each class. Specifically, the balanced argument will automatically weigh classes inversely proportional to their frequency:

$$w_j = \frac{n}{kn_{j}}$$

where \(w_j\) is the weight to class \(j\), \(n\) is the number of observations, \(n_j\) is the number of observations in class \(j\), and \(k\) is the total number of classes.


# Load libraries
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np

Load Iris Flower Dataset

# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target

Make Classes Imbalanced

# Make class highly imbalanced by removing first 40 observations
X = X[40:,:]
y = y[40:]

# Create target vector indicating if class 0, otherwise 1
y = np.where((y == 0), 0, 1)

Standardize Features

# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Train A Logistic Regression With Weighted Classes

# Create decision tree classifer object
clf = LogisticRegression(random_state=0, class_weight='balanced')

# Train model
model = clf.fit(X_std, y)