Book Image

Agile Machine Learning with DataRobot

By : Bipin Chadha, Sylvester Juwe
Book Image

Agile Machine Learning with DataRobot

By: Bipin Chadha, Sylvester Juwe

Overview of this book

DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.
Table of Contents (19 chapters)
1
Section 1: Foundations
5
Section 2: Full ML Life Cycle with DataRobot: Concept to Value
11
Section 3: Advanced Topics

Engineering features for modeling

As part of the system's understanding you would have gained some insights into your problem and dataset that can be used to create new features in your dataset by combining the existing features in various ways. For example, we can create a new feature called volume by multiplying length, width, and height. Similarly, we can create a feature called mpg-ratio by dividing highway-mpg by city-mpg. Let's also create a feature called cylinder-size by dividing engine-size by cylinder-count. The equations for these features are as follows:

  • volume = length * width * height
  • mpg-ratio = highway-mpg / city-mpg
  • cylinder-size = engine-size / cylinder-count

Figure 4.11 shows an example of what these feature values look like:

Figure 4.11 – Engineered features for the Automobile Dataset

As you can now see, many possibilities exist to create new features that could prove helpful in solving your problem...