Logisitic Regression, despite its name, is used as a classifier (puts items into cateogires). It uses a similar algorithm to linear regression, hence the name. The basic logistic regression is used as a binary classifier and can categorize items into two groups. In this article, we will learn how to build a Binary Classifier with Logisitic Regression in Sklearn.
To create a Binary Classifier, we use the
LogisticRegression class from the
linear_model package. We create an instance of this class, then pass our features (x values) and target (y value) to the
fit method. Below, we use a subset of the iris dataset to classify into two groups.
from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.preprocessing import StandardScaler iris = datasets.load_iris() features = iris.data[:100,:] target = iris.target[:100] logistic_regression = LogisticRegression() model = logistic_regression.fit(features, target) print(model.score())