Get cross validate score¶
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, iris.data, iris.target, cv=5) print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
>>> run script... Accuracy: 0.94 (+/- 0.15)