When you have large amounts of data, you will often need to select a different "solver" for your logistic regression model. The solver is part of the algorithm underneath that uses mathemicatlly optimization techniques. Sklearn allows you to choose models when buidling your model. In this article, we will learn how to train a logistic regression model on large data with Sklearn.
To change the solver for your logistic regression model, you simply need to specify the
solver paramter when creating an instance of
LogisticRegression. If you specify the
sag model, this will help you fit and classify on a large dataset.
# 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(solver="sag") model = logistic_regression.fit(features_standardized, target) print(mode.score())