Comparison of Naive Bayes, Semi-Supervised and ensemble models

 Comparison of Naive Bayes, Semi-Supervised & Ensemble methods  on output 



Python Program
>>> clf101=naive_bayes.BernoulliNB()
>>> clf102=tree.DecisionTreeClassifier()
>>> clf103=tree.ExtraTreeClassifier()

>>> from sklearn import ensemble
>>> clf104=ensemble.ExtraTreesClassifier()
>>> clf105=naive_bayes.GaussianNB()

>>> from sklearn import semi_supervised
>>> clf106=semi_supervised.LabelPropagation()
>>> clf107=semi_supervised.LabelSpreading()

>>> for clf in [clf101, clf102, clf103, clf104, clf105, clf106]:
...     clf.fit(x, y)
...     x_min, x_max = x[:, 0].min() -1, x[:, 0].max() +1
...     y_min, y_max = x[:, 1].min() -1, x[:, 1].max() +1
...     xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
...     z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
...     z = z.reshape(xx.shape)
...     plt.figure()
...     plt.pcolormesh(xx, yy, z, cmap=cmap_light)
...     plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=24)
...     plt.xlim(xx.min(), xx.max())
...     plt.ylim(yy.min(), yy.max())
...     plt.title("(clf='%s')" %(clf))
...

BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

ExtraTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
          max_features='auto', max_leaf_nodes=None,
          min_impurity_decrease=0.0, min_impurity_split=None,
          min_samples_leaf=1, min_samples_split=2,
          min_weight_fraction_leaf=0.0, random_state=None,
          splitter='random')

ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
           max_depth=None, max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)

GaussianNB(priors=None)

LabelPropagation(alpha=None, gamma=20, kernel='rbf', max_iter=1000, n_jobs=1,
         n_neighbors=7, tol=0.001)