.. _example_applications_plot_model_complexity_influence.py: ========================== Model Complexity Influence ========================== Demonstrate how model complexity influences both prediction accuracy and computational performance. The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for regression (resp. classification). For each class of models we make the model complexity vary through the choice of relevant model parameters and measure the influence on both computational performance (latency) and predictive power (MSE or Hamming Loss). .. rst-class:: horizontal * .. image:: images/\plot_model_complexity_influence_001.png :scale: 47 * .. image:: images/\plot_model_complexity_influence_002.png :scale: 47 * .. image:: images/\plot_model_complexity_influence_003.png :scale: 47 **Script output**:: Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.25, learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.050145s Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.5, learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.040136s Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.75, learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.035565s Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.9, learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.029460s Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05, kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001, verbose=False) Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000533s Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05, kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001, verbose=False) Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.001001s Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05, kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001, verbose=False) Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001668s Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05, kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001, verbose=False) Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.002235s Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05, kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001, verbose=False) Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.003136s Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) Complexity: 10 | MSE: 29.7323 | Pred. Time: 0.001268s Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=50, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) Complexity: 50 | MSE: 8.3021 | Pred. Time: 0.000434s Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) Complexity: 100 | MSE: 7.0518 | Pred. Time: 0.000801s Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=200, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) Complexity: 200 | MSE: 6.1267 | Pred. Time: 0.000867s Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls', max_depth=3, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) Complexity: 500 | MSE: 6.3365 | Pred. Time: 0.001935s **Python source code:** :download:`plot_model_complexity_influence.py ` .. literalinclude:: plot_model_complexity_influence.py :lines: 16- **Total running time of the example:** 50.26 seconds ( 0 minutes 50.26 seconds)