discrete branch: add a compelling example of discretization's benefits

We recently merged a discretizing transformer into the discrete branch (see diff between that branch and master). Before merging it into master, we’d like a compelling example for our example gallery showing an application of machine learning where discretized features are particularly useful.

To dear contributor: Make sure to submit a pull request to the discrete branch.

[qinhanmin2014]

@jnothman Regret me if the example is not good since I’m not an expert at machine learning 😃 The score is averaged over folds.

DecisionTree score before discretization : 0.946666666667
DecisionTree score std before discretization : 0.04
DecisionTree score after discretization : 0.96
DecisionTree score std after discretization : 0.0326598632371
SVC score before discretization : 0.96
SVC score std before discretization : 0.0249443825785
SVC score after discretization : 0.966666666667
SVC score std after discretization : 0.0249443825785

Since our discretization is naive, we cannot expect big improve. The experiment is designed mainly based on this paper (citation > 2000) and other materails. Here is part of the main code:

iris = load_iris()
X = iris.data
y = iris.target
X = X[:, [2,3]]
Xt = KBinsDiscretizer(n_bins=10, encode='ordinal').fit_transform(X)
clf1 = DecisionTreeClassifier(random_state=0)
print("DecisionTree score before discretization : {}"
      .format(np.mean(cross_val_score(clf1, X, y, cv=5))))
print("DecisionTree score std before discretization : {}"
      .format(np.std(cross_val_score(clf1, X, y, cv=5))))
clf2 = DecisionTreeClassifier(random_state=0)
print("DecisionTree score after discretization : {}"
      .format(np.mean(cross_val_score(clf2, Xt, y, cv=5))))
print("DecisionTree score std after discretization : {}"
      .format(np.std(cross_val_score(clf2, Xt, y, cv=5))))

[qinhanmin2014]

@jnothman (Sorry for the repeatedly update) Here is my plan for the example, please have a look. Thanks 😃 Dataset: iris (only use two features) (1)plot the data before and after discretization (2)train a classifier using the data before and after discretization and compare the result

DecisionTree score before discretization : 0.946666666667
DecisionTree score after discretization : 0.96
SVC score before discretization : 0.96
SVC score after discretization : 0.966666666667