Tutorial on warmstart grid search for multiple classifiers

[cshih14]

Solves issue #34

This tutorial tunes hyperparameters max_features and n_estimators for ExtraTrees and RandomForestClassifier in order to fairly compare their performance.

Included in Functions

• Given classifiers and dictionaries of two hyperparameters, finds the optimal pair of hyperparameter values for each classifier using grid search. In this specific example, max_features and n_estimators will be the parameters being tuned.

• Implements warmstart for searching through increasing numbers of estimators in the ensemble.

• Visualizes the performance of each parameter value pair in a heatmap to show more detail on how the optimal parameters were determined.

[j1c]

The notebook looks great! I think the last thing to change is to turn the ipynb file into a .py file. In sklearn, the .py files are used to build the examples page automatically, and they wont take ipynb files.