.. _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
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.. image:: images/plot_model_complexity_influence_001.png
:scale: 47
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.. image:: images/plot_model_complexity_influence_002.png
:scale: 47
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.. 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: 0.033256s
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: 0.024938s
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: 0.020205s
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: 0.018175s
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: 0.000508s
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: 0.000924s
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: 0.001564s
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: 0.002202s
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: 0.002535s
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: 28.9793 | Pred. Time: 0.000134s
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: 8.3398 | Pred. Time: 0.000234s
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: 7.0096 | Pred. Time: 0.000339s
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: 6.1836 | Pred. Time: 0.000558s
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: 6.3426 | Pred. Time: 0.001228s
**Python source code:** :download:`plot_model_complexity_influence.py <plot_model_complexity_influence.py>`
.. literalinclude:: plot_model_complexity_influence.py
:lines: 16-
**Total running time of the example:** 82.28 seconds
( 1 minutes 22.28 seconds)