Machine Learning for Beginners in Python: How to Handle Imbalanced Classes In Logistic Regression

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Handling Imbalanced Classes In Logistic Regression

Preliminaries


/* 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)

 

Python Example for Beginners

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