Get cross validate scoreΒΆ

The simplest way to use cross-validation is to call the cross_val_score helper function on the estimator and the dataset.

The following example demonstrates how to estimate the accuracy of a linear kernel support vector machine on the iris dataset by splitting the data, fitting a model and computing the score 5 consecutive times (with different splits each time. The mean score and the 95% confidence interval of the score estimate are hence given.

from miml import datasets
from miml.classification import SVM
from miml.model_selection import cross_val_score

iris = datasets.load_iris()
model = SVM(kernel='linear', C=1)
scores = cross_val_score(model,,, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
>>> run script...
Accuracy: 0.94 (+/- 0.15)