def gbm(interactive=True, echo=True, testing=False):
    """GBM model demo."""

    def demo_body(go):
        """
        Demo of H2O's Gradient Boosting estimator.

        This demo uploads a dataset to h2o, parses it, and shows a description.
        Then it divides the dataset into training and test sets, builds a GLM
        from the training set, and makes predictions for the test set.
        Finally, default performance metrics are displayed.
        """
        go()
        # Connect to H2O
        h2o.init()

        go()
        # Upload the prostate dataset that comes included in the h2o python package
        prostate = h2o.load_dataset("prostate")

        go()
        # Print a description of the prostate data
        prostate.describe()

        go()
        # Randomly split the dataset into ~70/30, training/test sets
        train, test = prostate.split_frame(ratios=[0.70])

        go()
        # Convert the response columns to factors (for binary classification problems)
        train["CAPSULE"] = train["CAPSULE"].asfactor()
        test["CAPSULE"] = test["CAPSULE"].asfactor()

        go()
        # Build a (classification) GLM
        from h2o.estimators import H2OGradientBoostingEstimator
        prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ntrees=10, max_depth=8,
                                                    min_rows=10, learn_rate=0.2)
        prostate_gbm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
                           y="CAPSULE", training_frame=train)

        go()
        # Show the model
        prostate_gbm.show()

        go()
        # Predict on the test set and show the first ten predictions
        predictions = prostate_gbm.predict(test)
        predictions.show()

        go()
        # Fetch a tree, print number of tree nodes, show root node description
        from h2o.tree import H2OTree, H2ONode
        tree = H2OTree(prostate_gbm, 0, "0")
        len(tree)
        tree.left_children
        tree.right_children
        tree.root_node.show()

        go()
        # Show default performance metrics
        performance = prostate_gbm.model_performance(test)
        performance.show()

    # Execute:
    _run_demo(demo_body, interactive, echo, testing)
def gbm(interactive=True, echo=True, testing=False):
    

    def demo_body(go):
        
        go()
        
        h2o.init()

        go()
        
        prostate = h2o.load_dataset("prostate")

        go()
        
        prostate.describe()

        go()
        
        train, test = prostate.split_frame(ratios=[0.70])

        go()
        
        train["CAPSULE"] = train["CAPSULE"].asfactor()
        test["CAPSULE"] = test["CAPSULE"].asfactor()

        go()
        
        from h2o.estimators import H2OGradientBoostingEstimator
        prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ntrees=10, max_depth=8,
                                                    min_rows=10, learn_rate=0.2)
        prostate_gbm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
                           y="CAPSULE", training_frame=train)

        go()
        
        prostate_gbm.show()

        go()
        
        predictions = prostate_gbm.predict(test)
        predictions.show()

        go()
        
        from h2o.tree import H2OTree, H2ONode
        tree = H2OTree(prostate_gbm, 0, "0")
        len(tree)
        tree.left_children
        tree.right_children
        tree.root_node.show()

        go()
        
        performance = prostate_gbm.model_performance(test)
        performance.show()

    
    _run_demo(demo_body, interactive, echo, testing)
def gbm ( interactive = True , echo = True , testing = False ) : def demo_body ( go ) : """
        Demo of H2O's Gradient Boosting estimator.

        This demo uploads a dataset to h2o, parses it, and shows a description.
        Then it divides the dataset into training and test sets, builds a GLM
        from the training set, and makes predictions for the test set.
        Finally, default performance metrics are displayed.
        """ go ( ) # Connect to H2O h2o . init ( ) go ( ) # Upload the prostate dataset that comes included in the h2o python package prostate = h2o . load_dataset ( "prostate" ) go ( ) # Print a description of the prostate data prostate . describe ( ) go ( ) # Randomly split the dataset into ~70/30, training/test sets train , test = prostate . split_frame ( ratios = [ 0.70 ] ) go ( ) # Convert the response columns to factors (for binary classification problems) train [ "CAPSULE" ] = train [ "CAPSULE" ] . asfactor ( ) test [ "CAPSULE" ] = test [ "CAPSULE" ] . asfactor ( ) go ( ) # Build a (classification) GLM from h2o . estimators import H2OGradientBoostingEstimator prostate_gbm = H2OGradientBoostingEstimator ( distribution = "bernoulli" , ntrees = 10 , max_depth = 8 , min_rows = 10 , learn_rate = 0.2 ) prostate_gbm . train ( x = [ "AGE" , "RACE" , "PSA" , "VOL" , "GLEASON" ] , y = "CAPSULE" , training_frame = train ) go ( ) # Show the model prostate_gbm . show ( ) go ( ) # Predict on the test set and show the first ten predictions predictions = prostate_gbm . predict ( test ) predictions . show ( ) go ( ) # Fetch a tree, print number of tree nodes, show root node description from h2o . tree import H2OTree , H2ONode tree = H2OTree ( prostate_gbm , 0 , "0" ) len ( tree ) tree . left_children tree . right_children tree . root_node . show ( ) go ( ) # Show default performance metrics performance = prostate_gbm . model_performance ( test ) performance . show ( ) # Execute: _run_demo ( demo_body , interactive , echo , testing )
GBM model demo.
GBM model demo .