Branch dev vectorization feature

classifiers.py

@@ -14,15 +14,15 @@ classifiers = [
                 ('bayes', MultinomialNB()),
                 ('lr', LogisticRegression()),
                 ('sgd', SGDClassifier()),
-                ('svm', SVC() ),
-                ('decisionTree',DecisionTreeClassifier()),
+                #('decisionTree',DecisionTreeClassifier()),
                 ('rfc', RandomForestClassifier()),
-                ('knn', KNeighborsClassifier())
+                ('knn', KNeighborsClassifier()),
+                ('svm', SVC() )
                 ]
 
 
-param_grid_svm = {'C':[1,10,100,1000],'gamma':[1,0.1,0.001,0.0001], 'kernel':['linear','rbf']}
-param_grid_decisionTree = { 'criterion' : ['gini', 'entropy'], 'max_depth':range(5,10), 'min_samples_split': range(5,10), 'min_samples_leaf': range(1,5) }
+param_grid_svm = {'C':[1,10,100,1000],'gamma':[0.1,0.001,0.0001], 'kernel':['linear','rbf']}
+#param_grid_decisionTree = { 'criterion' : ['gini', 'entropy'], 'max_depth':range(5,10), 'min_samples_split': range(5,10), 'min_samples_leaf': range(1,5) }
 param_grid_rfc = { 'n_estimators': [200, 500], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth' : [4,5,6,7,8], 'criterion' :['gini', 'entropy'] }
 param_grid_lr = {"C":np.logspace(-3,3,7), "penalty":['none',"l2"]}
 param_grid_sgd = { "loss" : ["hinge", "log", "squared_hinge", "modified_huber"], "alpha" : [0.0001, 0.001, 0.01, 0.1], "penalty" : ["l2", "l1", "none"], "max_iter" : [500]}