python-scikit-learnUsing quantile regression

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.QuantileRegressor()
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	`.QuantileRegressor(` creates linear regression model object that predicts conditional quantiles
`.fit(` train model	`.predict(` predict values

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

output

-0.10712746464335843

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