When working with data sets, you may come accross data with imbalanced classes, mean that you have more sample of one classe than another. For example, you maybe have more male samples then female. In such cases, it is wise to use imbalance options. In this article, we will learn how to handle imbalanced classes with Logistic Regression in Sklearn.
To handle imbalanced classes with logistic regression, we use the
class_weight option and set the
balanced value. This will tell sklearn to use stratified sampling techniques and other alogrithms to handle imabalanced classes and fit a better model.
# from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.preprocessing import StandardScaler iris = datasets.load_iris() features = iris.data target = iris.target scaler = StandardScaler() features_standardized = scaler.fit_transform(features) logistic_regression = LogisticRegression(class_weight="balanced") model = logistic_regression.fit(features_standardized, target) print(mode.score())