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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 + 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

.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