As we have seen, every estimator exposes a
score method that can judge the quality of the fit (or the prediction) on new data. Bigger is better.
>>> from sklearn import datasets, svm >>> digits = datasets.load_digits() >>> X_digits = digits.data >>> y_digits = digits.target >>> svc = svm.SVC(C=1, kernel='linear') >>> svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 0.97999999999999998
To get a better measure of prediction accuracy (which we can use as a proxy for goodness of fit of the model), we can successively split the data in folds that we use for training and testing:
>>> import numpy as np >>> X_folds = np.array_split(X_digits, 3) >>> y_folds = np.array_split(y_digits, 3) >>> scores = list() >>> for k in range(3): ... # We use 'list' to copy, in order to 'pop' later on ... X_train = list(X_folds) ... X_test = X_train.pop(k) ... X_train = np.concatenate(X_train) ... y_train = list(y_folds) ... y_test = y_train.pop(k) ... y_train = np.concatenate(y_train) ... scores.append(svc.fit(X_train, y_train).score(X_test, y_test)) >>> print(scores) [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]
This is called a
Scikit-learn has a collection of classes which can be used to generate lists of train/test indices for popular cross-validation strategies.
They expose a
split method which accepts the input dataset to be split and yields the train/test set indices for each iteration of the chosen cross-validation strategy.
This example shows an example usage of the
>>> from sklearn.model_selection import KFold, cross_val_score >>> X = ["a", "a", "b", "c", "c", "c"] >>> k_fold = KFold(n_splits=3) >>> for train_indices, test_indices in k_fold.split(X): ... print('Train: %s | test: %s' % (train_indices, test_indices)) Train: [2 3 4 5] | test: [0 1] Train: [0 1 4 5] | test: [2 3] Train: [0 1 2 3] | test: [4 5]
The cross-validation can then be performed easily:
>>> [svc.fit(X_digits[train], y_digits[train]).score(X_digits[test], y_digits[test]) ... for train, test in k_fold.split(X_digits)] [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]
The cross-validation score can be directly calculated using the
cross_val_score helper. Given an estimator, the cross-validation object and the input dataset, the
cross_val_score splits the data repeatedly into a training and a testing set, trains the estimator using the training set and computes the scores based on the testing set for each iteration of cross-validation.
By default the estimator’s
score method is used to compute the individual scores.
Refer the metrics module to learn more on the available scoring methods.
>>> cross_val_score(svc, X_digits, y_digits, cv=k_fold, n_jobs=-1) array([ 0.93489149, 0.95659432, 0.93989983])
n_jobs=-1 means that the computation will be dispatched on all the CPUs of the computer.
scoring argument can be provided to specify an alternative scoring method.
>>> cross_val_score(svc, X_digits, y_digits, cv=k_fold, ... scoring='precision_macro') array([ 0.93969761, 0.95911415, 0.94041254])
|Splits it into K folds, trains on K-1 and then tests on the left-out.||Same as K-Fold but preserves the class distribution within each fold.||Ensures that the same group is not in both testing and training sets.|
|| || |
|Generates train/test indices based on random permutation.||Same as shuffle split but preserves the class distribution within each iteration.||Ensures that the same group is not in both testing and training sets.|
|Takes a group array to group observations.||Leave P groups out.||Leave one observation out.|
|Leave P observations out.||Generates train/test indices based on predefined splits.|
On the digits dataset, plot the cross-validation score of a
SVC estimator with an linear kernel as a function of parameter
C (use a logarithmic grid of points, from 1 to 10).
import numpy as np from sklearn.model_selection import cross_val_score from sklearn import datasets, svm digits = datasets.load_digits() X = digits.data y = digits.target svc = svm.SVC(kernel='linear') C_s = np.logspace(-10, 0, 10)
scikit-learn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. This object takes an estimator during the construction and exposes an estimator API:
>>> from sklearn.model_selection import GridSearchCV, cross_val_score >>> Cs = np.logspace(-6, -1, 10) >>> clf = GridSearchCV(estimator=svc, param_grid=dict(C=Cs), ... n_jobs=-1) >>> clf.fit(X_digits[:1000], y_digits[:1000]) GridSearchCV(cv=None,... >>> clf.best_score_ 0.925... >>> clf.best_estimator_.C 0.0077... >>> # Prediction performance on test set is not as good as on train set >>> clf.score(X_digits[1000:], y_digits[1000:]) 0.943...
By default, the
GridSearchCV uses a 3-fold cross-validation. However, if it detects that a classifier is passed, rather than a regressor, it uses a stratified 3-fold.
>>> cross_val_score(clf, X_digits, y_digits) ... array([ 0.938..., 0.963..., 0.944...])
Two cross-validation loops are performed in parallel: one by the
GridSearchCV estimator to set
gamma and the other one by
cross_val_score to measure the prediction performance of the estimator. The resulting scores are unbiased estimates of the prediction score on new data.
You cannot nest objects with parallel computing (
n_jobs different than 1).
Cross-validation to set a parameter can be done more efficiently on an algorithm-by-algorithm basis. This is why, for certain estimators, scikit-learn exposes Cross-validation: evaluating estimator performance estimators that set their parameter automatically by cross-validation:
>>> from sklearn import linear_model, datasets >>> lasso = linear_model.LassoCV() >>> diabetes = datasets.load_diabetes() >>> X_diabetes = diabetes.data >>> y_diabetes = diabetes.target >>> lasso.fit(X_diabetes, y_diabetes) LassoCV(alphas=None, copy_X=True, cv=None, eps=0.001, fit_intercept=True, max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False, precompute='auto', random_state=None, selection='cyclic', tol=0.0001, verbose=False) >>> # The estimator chose automatically its lambda: >>> lasso.alpha_ 0.01229...
These estimators are called similarly to their counterparts, with ‘CV’ appended to their name.
On the diabetes dataset, find the optimal regularization parameter alpha.
Bonus: How much can you trust the selection of alpha?
from sklearn import datasets from sklearn.linear_model import LassoCV from sklearn.linear_model import Lasso from sklearn.model_selection import KFold from sklearn.model_selection import GridSearchCV diabetes = datasets.load_diabetes()
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