python-scikit-learnUsing Ridge regression model example

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.Ridge()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`datasets.load_diabetes` loads sample diabetes database
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.Ridge(` creates Ridge (linear least squares with l2 regularization) model
`.fit(` train transformation model	`.predict(` predict target variable based on given features dataset

Usage example

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.Ridge()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(model.score(X_test, y_test))

output

0.4273408113027499

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