'exhaustive search feature selection for regression model using root mean square error as a paraemter

from sklearn.linear_model import LinearRegression
from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS
from sklearn.metrics import mean_squared_error

lr = LinearRegression()

efs = EFS(lr,
min_features=1,
max_features=1,
scoring='neg_mean_squared_error',
cv=5)

efs.fit(X_train1,y_train1)

print('Best MSE score: %.2f' % efs.best_score_*(-1) )
print('Best subset:', efs.best_idx_)
print('Best subset (corresponding names):', efs.best_feature_names_)

I can't print the mean square error in output. how can I print/show the mean sqaure error or use root mean square error as a scoring parameter?

Solution 1:^[1]

Print score on testing samples

print(efs.score(x_test, y_test))

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Solution 1	pyaj

'exhaustive search feature selection for regression model using root mean square error as a paraemter

Solution 1:[1]

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Related Questions

Solution 1:^[1]