.. _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: 4488 | Hamming Loss (Misclassification Ratio): 0.2586 | Pred. Time: 0.027681s
  
  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: 0.019802s
  
  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: 0.016059s
  
  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: 0.014119s
  
  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: 0.000405s
  
  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: 0.001287s
  
  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: 0.001823s
  
  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: 0.002086s
  
  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: 0.000092s
  
  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: 0.000254s
  
  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: 0.000437s
  
  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: 0.001158s



**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:**  20.34 seconds
( 0 minutes  20.34 seconds)