scikit-learn¶

Models and Model Selection¶

Import

Function

Section

Description

sklearn.model_selection

train_test_split(*arrays, test_size=0.2)

Modeling and Estimation

Returns two random subsets of each array passed in, with 0.8 of the array in the first subset and 0.2 in the second subset

sklearn.linear_model

LinearRegression()

Modeling and Estimation

Returns an ordinary least squares Linear Regression model

sklearn.linear_model

LassoCV()

Modeling and Estimation

Returns a Lasso (L1 Regularization) linear model with picking the best model by cross validation

sklearn.linear_model

RidgeCV()

Modeling and Estimation

Returns a Ridge (L2 Regularization) linear model with picking the best model by cross validation

sklearn.linear_model

ElasticNetCV()

Modeling and Estimation

Returns a ElasticNet (L1 and L2 Regularization) linear model with picking the best model by cross validation

sklearn.linear_model

LogisticRegression()

Modeling and Estimation

Returns a Logistic Regression classifier

sklearn.linear_model

LogisticRegressionCV()

Modeling and Estimation

Returns a Logistic Regression classifier with picking the best model by cross validation

Working with a Model¶

Assuming you have a model variable that is a scikit-learn object:

Function

Section

Description

model.fit(X, y)

Modeling and Estimation

Fits the model with the X and y passed in

model.predict(X)

Modeling and Estimation

Returns predictions on the X passed in according to the model

model.score(X, y)

Modeling and Estimation

Returns the accuracy of X predictions based on the corect values (y)