Gain insights into H2O-3's scalable framework and explore model interpretability, AutoML features, and algorithm implementation. Discover how to derive insights and tackle big data for explainable ML solutions.

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This course teaches about a distributed and highly scalable machine learning framework known as H2O-3. H2O has become the go-to solution for organizations seeking to build data-driven and explainable solutions. In this course, you’ll learn about H2O’s versatile machine learning framework, which empowers you with high-performing and interpretable machine learning models. 

The H2O framework is compatible with Java, JSON, R, Python, and Scala. You’ll start by covering the concepts of machine learning models. As you progress, you’ll explore H2O’s core strengths: its focus on model interpretability and the efficiency-enhancing AutoML features. You’ll get to implement supervised and unsupervised algorithms and derive insights from them. 

This course will equip you with expertise that will advance your career by making them valuable for tackling big data, creating explainable models, and automating ML tasks, opening doors in various industries.

Distributed Machine Learning and Its Implementation with H2O

import h2o
import pandas as pd
from h2o.estimators import H2OGradientBoostingEstimator as H2OGBM
from sklearn.metrics import auc
import numpy as np
h2o.init(port=8080)

filepath = "Data/Classification/"
data = h2o.import_file(filepath+'Lending_Club_Loans.csv')

# Converting the H2O Frame to a Pandas DataFrame
data = data.as_data_frame()

# interaction feature "PaymentToIncome" based on domain knowledge
data['PaymentToIncome'] = (data['installment']*12.0)/data['annual_inc']

# new feature by transforming few continous variable to categorical ones
data['dti_cat'] = pd.qcut(data['dti'], q=[0, .25, .5, .75, 1.], labels=['Low','Moderate','Medium','High'])
data['int_rate_cat'] = pd.qcut(data['int_rate'], q=[0, .25, .5, .75, 1.], labels=['Low','Moderate','Medium','High'])

# changing existing revol_util feature from continous to categorical
data['revol_util'] = pd.qcut(data['revol_util'], q=[0, .25, .5, .75, 1.], labels=['Low','Moderate','Medium','High'])

data = h2o.H2OFrame(data)

# Checking the set of input features once again.
X = list(set(data.names) - set("loan_status")) # removing target from the list of all columns
y = "loan_status"

splits = data.split_frame(ratios = [0.70, 0.15], seed = 1)
train = splits[0]
valid = splits[1]
test = splits[2]

# Set up the H2O GBM parameters
gbm = H2OGBM(max_runtime_secs=40, nfolds=0, seed=42,
            stopping_rounds=5,
            ntrees= 1000,
            max_depth = 5,
            sample_rate= 0.64, 
            col_sample_rate= 0.67, 
            col_sample_rate_per_tree= 0.71, 
            col_sample_rate_change_per_level= 1.01,
            score_tree_interval=5,
            stopping_metric='AUC')

# Train the model
gbm.train(x=X, y=y, training_frame=train, validation_frame=valid)
print(gbm.model_performance(test_data=test))

Understand how to add new features to help predict loan status.

Introduction to Machine Learning

Supervised Learning: Regression Models with H2O

Supervised Learning: Classification Models with H2O

Unsupervised Learning: Clustering with H2O

Unsupervised Learning: Anomaly Detection with H2O

Closing Notes

Appendix

Solution: Classifier Model Feature Engineering

The magic unfolds!