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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 + c
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