python-scikit-learnHow to calculate MAPE (mean absolute percentage error)
from sklearn import datasets, linear_model, model_selection, metrics
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.LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mape = metrics.mean_absolute_percentage_error(y_test, y_pred)ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
model_selection.train_test_splitsplits given | .fit(train model with a given features and target variable dataset |
.predict(predict target variable based on given features dataset | .mean_absolute_percentage_error(calculates MAPE for the trained model |
Usage example
from sklearn import datasets, linear_model, model_selection, metrics
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.LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mape = metrics.mean_absolute_percentage_error(y_test, y_pred)
print(mape)output
0.3677663393932409
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