def _make_predictions(self):
"""Make predictions on test data"""
test_data = clean_design_matrix(test_survived)
predictors = [pred for pred in self.predictors if pred in list(
test_data.columns.values)]
model = self._build_model(self.design_matrix, predictors)
predictions = model.predict(test_data[predictors])
return model, predictions
def _make_predictions_using_cv_fit(self):
"""Make predictions on test data"""
test_data = clean_design_matrix(test_rental)
predictors = [pred for pred in self.predictors if pred in list(
test_data.columns.values)]
model = self._build_model(self.design_matrix, predictors)
predictions = model.predict_proba(test_data[predictors])
return model, predictions
def _make_predictions(self):
"""Make predictions on test data"""
test_data = clean_design_matrix(test_survived)
predictors = [
pred for pred in self.predictors
if pred in list(test_data.columns.values)
]
model = self._build_model(self.design_matrix, predictors)
predictions = model.predict(test_data[predictors])
feature_imp = pd.DataFrame(columns=predictors)
feature_imp.loc[0] = model.feature_importances_
return model, predictions, feature_imp
def _make_predictions_using_cv_fit(self):
"""Make predictions on test data"""
test_data = clean_design_matrix(test_rental)
predictors = [
pred for pred in self.predictors
if pred in list(test_data.columns.values)
]
feature_imp = pd.DataFrame(columns=predictors)
model = self._build_model(self.design_matrix, predictors)
feature_imp.loc[0] = model.feature_importances_
predictions = model.predict_proba(test_data[predictors])
return model, predictions, feature_imp
def __init__(self):
self.design_matrix = clean_design_matrix(train_survived, train=True)
self.predictors = [ele for ele in list(
self.design_matrix.columns.values) if ele not in [
'PassengerId', 'Survived']]
def __init__(self):
self.design_matrix = clean_design_matrix(train_rental, train=True)
self.predictors = [
ele for ele in list(self.design_matrix.columns.values)
if ele not in ['interest_level']
]
