output_vector_items=output_features)
classification_tf = RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=2,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=10,
n_jobs=1,
oob_score=False,
random_state=0,
verbose=0,
warm_start=False)
classification_tf.mlinit(input_features="features",
prediction_column='species',
feature_names="features")
rf_pipeline = Pipeline([(feature_extractor_tf.name, feature_extractor_tf),
(classification_tf.name, classification_tf)])
rf_pipeline.mlinit()
rf_pipeline.fit(data[input_features], data['species'])
rf_pipeline.serialize_to_bundle('./', 'mleap-scikit-rf-pipeline', init=True)
output_vector=output_vector_name,
output_vector_items=output_features)
classification_tf = RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=2,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=10,
n_jobs=1,
oob_score=False,
random_state=0,
verbose=0,
warm_start=False)
classification_tf.mlinit(input_features=feature_extractor_tf.output_vector,
prediction_column='Survived',
feature_names="features")
rf_pipeline = Pipeline([(feature_extractor_tf.name, feature_extractor_tf),
(classification_tf.name, classification_tf)])
rf_pipeline.mlinit()
rf_pipeline.fit(train[input_features], train['Survived'])
rf_pipeline.serialize_to_bundle('./models', 'random_forest', init=True)