.. _example_neural_networks_plot_mlp_alpha.py: ================================================ Varying regularization in Multi-layer Perceptron ================================================ A comparison of different values for regularization parameter 'alpha' on synthetic datasets. The plot shows that different alphas yield different decision functions. Alpha is a parameter for regularization term, aka penalty term, that combats overfitting by constraining the size of the weights. Increasing alpha may fix high variance (a sign of overfitting) by encouraging smaller weights, resulting in a decision boundary plot that appears with lesser curvatures. Similarly, decreasing alpha may fix high bias (a sign of underfitting) by encouraging larger weights, potentially resulting in a more complicated decision boundery. .. image:: images/plot_mlp_alpha_001.png :align: center **Python source code:** :download:`plot_mlp_alpha.py <plot_mlp_alpha.py>` .. literalinclude:: plot_mlp_alpha.py :lines: 18- **Total running time of the example:** 3.89 seconds ( 0 minutes 3.89 seconds)