python-scikit-learnHow to evaluate a score by cross validation based on cross_val_score()
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
cvs = cross_val_score(lasso, X, y)ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
linear_model.Lassocreates Lasso model | cross_val_score(evaluates a score by cross-validation |
lassomodel to use for cross-validation | |
Usage example
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
cvs = cross_val_score(lasso, X, y)
print(cvs)output
[0.29828675 0.2241492 0.15480127 0.25519733 0.17108715]
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