.. _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: 4488 | Hamming Loss (Misclassification Ratio): 0.2586 | Pred. Time: 0.029275s
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: 1650 | Hamming Loss (Misclassification Ratio): 0.2875 | Pred. Time: 0.020339s
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: 893 | Hamming Loss (Misclassification Ratio): 0.3286 | Pred. Time: 0.016850s
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: 666 | Hamming Loss (Misclassification Ratio): 0.3281 | Pred. Time: 0.014667s
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: 0.000412s
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: 0.000745s
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: 0.001311s
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: 0.001847s
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: 0.002115s
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,
random_state=None, subsample=1.0, verbose=0, warm_start=False)
Complexity: 10 | MSE: 27.9672 | Pred. Time: 0.000075s
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,
random_state=None, subsample=1.0, verbose=0, warm_start=False)
Complexity: 50 | MSE: 8.0288 | Pred. Time: 0.000161s
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,
random_state=None, subsample=1.0, verbose=0, warm_start=False)
Complexity: 100 | MSE: 6.7578 | Pred. Time: 0.000252s
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,
random_state=None, subsample=1.0, verbose=0, warm_start=False)
Complexity: 200 | MSE: 5.8592 | Pred. Time: 0.000438s
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,
random_state=None, subsample=1.0, verbose=0, warm_start=False)
Complexity: 500 | MSE: 6.0492 | Pred. Time: 0.001019s
**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:** 22.11 seconds
( 0 minutes 22.11 seconds)