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Machine Learning For Dummies
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If estimate variance is high and your algorithm is relying on many features (tree-based algorithms choose features they learn from), you need to prune some features for better results. In this context, reducing the number of features in your data matrix by picking those with the highest predictive value is advisable.
When working with linear models, linear support vector machines, or neural networks, regularization is always an option. Both L1 and L2 can reduce the influence of redundant variables or even remove them from the model (see the “Solving overfitting by using selection” section of Chapter 15). Stability selection leverages the L1 ability to exclude less useful variables. The technique resamples the training data to confirm the exclusion.
You can learn more about stability selection by viewing the example on the Scikit-learn website: http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html. In addition...
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