<jupyter_start><jupyter_text>col = "TotalCharges"print(train[col].dtype)train = GF.fix_exceptions_in_float_column(train, col)train[col].dtype<jupyter_code># split into input and output elements
#train, test = train_test_split(train, test_size=0.2, random_state=99)
filename = 'titanic_test.csv'
url = datapath + filename
test = pd.read_csv(url, header=0, sep=sep)
print(train.shape, test.shape)
test.head(1)
keras_model_type =  "auto" ## always try "fast1" first, then "fast2", then "auto".
##  If NLP, use "BERT" and if image, use "image" for keras_model_type. ###
model_options = {"tuner":"optuna", 'nlp_char_limit':10, 'cat_feat_cross_flag':False, 'max_trials': 5}
keras_options = {"patience":10,'class_weight':True, "early_stopping": True, "epochs": 100}
output = deepauto.fit(train, target, keras_model_type,project_name=project_name, keras_options=keras_options,
                 model_options=model_options, save_model_flag=False, use_my_model='', verbose=1)
model = output[0]
cat_vocab_dict = output[1]
y_preds = deepauto.predict(model, project_name, test_dataset=test,
                                 keras_model_type=keras_model_type, cat_vocab_dict=cat_vocab_dict)
y_test = test[target].values
y_test.shape
y_preds[0].shape
from deep_autoviml.utilities.utilities import print_classification_model_stats, print_regression_model_stats
#print_regression_model_stats(y_test, y_preds[0])
print_classification_model_stats(y_test, y_preds[1])<jupyter_output>Balanced Accuracy = 82.95%
Confusion Matrix:
[[30  3]
 [ 5 15]]
              precision    recall  f1-score   support

           0       0.86      0.91      0.88        33
           1       0.83      0.75      0.79        20

    accuracy                           0.85        53
   macro avg       0.85      0.83      0.84        53
weighted avg       0.85      0.85      0.85        53

#####################################################################
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