modified ml params and models

classification_names_params.py

@@ -32,7 +32,7 @@ names = [
 ]
 
 classifiers = [
-    KNeighborsClassifier(3),
+    KNeighborsClassifier(5),
     SVC(kernel="linear", C=0.025),
     SVC(gamma=2, C=1),
     GaussianProcessClassifier(1.0 * RBF(1.0)),
@@ -97,7 +97,7 @@ classifiers = [
     ))
     
     gs_knn_params = {
-    'clf__n_neighbors': [3, 7, 10]
+    'clf__n_neighbors': [5, 7, 11]
     #, 'clf__n_neighbors': range(1, 21, 2)
     ,'clf__metric'     : ['euclidean', 'manhattan', 'minkowski']
     , 'clf__weights'    : ['uniform', 'distance']
@@ -120,7 +120,7 @@ classifiers = [
         , 'clf__min_samples_leaf': [2, 4, 8, 50]
         , 'clf__min_samples_split': [10, 20]
         }
-#%% XGBClassifier()
+#%% XGBClassifier() # SPNT
 # https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
 # https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
     gs_xgb = Pipeline((
@@ -135,6 +135,7 @@ classifiers = [
         , 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
         , 'clf__max_features': ['auto', 'sqrt']
         }  
+        
 #%% MLPClassifier()
 # https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
     gs_mlp = Pipeline((
@@ -190,6 +191,7 @@ classifiers = [
        # If None, then the base estimator is a DecisionTreeClassifier.
        , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()']# if none, DT is used
        , 'clf__gamma': ['scale', 'auto'] }
+       
 #%% GradientBoostingClassifier()
 # https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
     gs_gb = Pipeline((
@@ -198,7 +200,7 @@ classifiers = [
       ))
 
     gs_bdt_params = {
-        'clf__n_estimators'   : [10, 100, 1000]
+        'clf__n_estimators'   : [10, 100, 200, 500, 1000]
         , 'clf__n_estimators' : [10, 100, 1000]
         , 'clf__learning_rate': [0.001, 0.01, 0.1]
         , 'clf__subsample'    : [0.5, 0.7, 1.0]
@@ -261,4 +263,4 @@ BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
         , 'clf__binarize':['None', 0]
         , 'clf__fit_prior': [True]
         , 'clf__class_prior': ['None']
-        }
+        }