File Content: plot_cv_diabetes.py """ =============================================== Cross-validation on diabetes Dataset Exercise =============================================== A tutorial exercise which uses cross-validation with linear models. This exercise is used in the :ref:`cv_estimators_tut` part of the :ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`. """ from __future__ import print_function print(__doc__) import numpy as np import matplotlib.pyplot as plt 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 cross_val_score diabetes = datasets.load_diabetes() X = diabetes.data[:150] y = diabetes.target[:150] lasso = Lasso(random_state=0) alphas = np.logspace(-4, -0.5, 30) scores = list() scores_std = list() n_folds = 3 for alpha in alphas: lasso.alpha = alpha this_scores = cross_val_score(lasso, X, y, cv=n_folds, n_jobs=1) scores.append(np.mean(this_scores)) scores_std.append(np.std(this_scores)) scores, scores_std = np.array(scores), np.array(scores_std) plt.figure().set_size_inches(8, 6) plt.semilogx(alphas, scores) # plot error lines showing +/- std. errors of the scores std_error = scores_std / np.sqrt(n_folds) plt.semilogx(alphas, scores + std_error, 'b--') plt.semilogx(alphas, scores - std_error, 'b--') # alpha=0.2 controls the translucency of the fill color plt.fill_between(alphas, scores + std_error, scores - std_error, alpha=0.2) plt.ylabel('CV score +/- std error') plt.xlabel('alpha') plt.axhline(np.max(scores), linestyle='--', color='.5') plt.xlim([alphas[0], alphas[-1]]) ############################################################################## # Bonus: how much can you trust the selection of alpha? # To answer this question we use the LassoCV object that sets its alpha # parameter automatically from the data by internal cross-validation (i.e. it # performs cross-validation on the training data it receives). # We use external cross-validation to see how much the automatically obtained # alphas differ across different cross-validation folds. lasso_cv = LassoCV(alphas=alphas, random_state=0) k_fold = KFold(3) print("Answer to the bonus question:", "how much can you trust the selection of alpha?") print() print("Alpha parameters maximising the generalization score on different") print("subsets of the data:") for k, (train, test) in enumerate(k_fold.split(X, y)): lasso_cv.fit(X[train], y[train]) print("[fold {0}] alpha: {1:.5f}, score: {2:.5f}". format(k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test]))) print() print("Answer: Not very much since we obtained different alphas for different") print("subsets of the data and moreover, the scores for these alphas differ") print("quite substantially.") plt.show() Submit