Machine Learning Pocket Reference by Matt Harrison

Author:Matt Harrison
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2019-09-03T16:00:00+00:00

>>> from yellowbrick.model_selection import ( ... ValidationCurve, ... ) >>> fig, ax = plt.subplots(figsize=(6, 4)) >>> vc_viz = ValidationCurve( ... RandomForestClassifier(n_estimators=100), ... param_name="max_depth", ... param_range=np.arange(1, 11), ... cv=10, ... n_jobs=-1, ... ) >>> vc_viz.fit(X, y) >>> vc_viz.poof() >>> fig.savefig("images/mlpr_1101.png", dpi=300)

Figure 11-1. Validation curve report.

The ValidationCurve class supports a scoring parameter. The parameter can be a custom function or one of the following options, depending on the task.

Classification scoring options include: 'accuracy', 'average_precision', 'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', and 'roc_auc'.

Clustering scoring options: 'adjusted_mutual_info_score', 'adjusted_rand_score', 'completeness_score', 'fowlkesmallows_score', 'homogeneity_score', 'mutual_info_score', 'normalized_mutual_info_score', and 'v_measure_score'.

Regression scoring options: 'explained_variance', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', and 'r2'.

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