In a previous article, we learned how to build a binary logistic regression model to classify items into one of two categories. This represents the basic model. Now, we want to extend our logistic regression model to classify with multiple categories. In this article, we will learn how to build a multi classifier with logisitc regression in Sklearn.
To extend logistic regression to classify with multiple categories, we fit a logisitc regression model as normally by creating an instance of the
LogisticRegression class and passing our features and target to the
fit method. However, when creating the
LogisticRegression instance we pass the
multi_class = "ovr" option. This will tell sklearn to use the ovr algorithm.
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(multi_class = "ovr") model = logistic_regression.fit(features_standardized, target) print(model.score())