python-scikit-learnSVR regression example
from sklearn import datasets, svm, 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 = svm.SVR()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
model_selection.train_test_splitsplits given | svm.SVR(creates Epsilon-Support Vector Regression model |
.fit(train transformation model | .predict(predict target variable based on given features dataset |
Usage example
from sklearn import datasets, svm, 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 = svm.SVR()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
0.192822596074521
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