udpate

classifiers.py

@@ -12,11 +12,11 @@ import numpy as np
 
 classifiers = [
                 ('bayes', MultinomialNB()),
+                ('lr', LogisticRegression()),
+                ('sgd', SGDClassifier()),
                 ('svm', SVC() ),
                 ('decisionTree',DecisionTreeClassifier()),
                 ('rfc', RandomForestClassifier()),
-                ('lr', LogisticRegression()),
-                ('sgd', SGDClassifier()),
                 ('knn', KNeighborsClassifier())
                 ]
 
@@ -26,7 +26,7 @@ param_grid_decisionTree = { 'criterion' : ['gini', 'entropy'], 'max_depth':range
 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":["l1","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]}
-param_grid_knn = {'n_neighbors' : list(range(1,20)), 'weights' : ['uniform', 'distance'], 'metric' : ['euclidean', 'manhattan'] }
+param_grid_knn = {'n_neighbors' : list(range(3,20)), 'weights' : ['uniform', 'distance'], 'metric' : ['euclidean', 'manhattan'] }
 
 grid_params = [
                 ('bayes', None),