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

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)