snapshot...

modules/machine_learning/classification.py

@@ -16,7 +16,7 @@ def get_csv_as_dataframe(csv_file):
     return icing_df
 
 
-def get_train_test_data(data_frame, normalize=True):
+def get_train_test_data(data_frame, standardize=True):
     icing_df = data_frame
     # The independent variables we want to use:
     params = ['cld_geo_thick', 'cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp',
@@ -32,7 +32,7 @@ def get_train_test_data(data_frame, normalize=True):
     print(icing_df.shape)
 
     x = np.asarray(icing_df[params])
-    if normalize:
+    if standardize:
         x = preprocessing.StandardScaler().fit(x).transform(x)
     y = np.asarray(icing_df['icing_intensity'])
     y = np.where(y == -1, 0, y)