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

Generating model explanations

Another key capability of DataRobot is that it automatically generates instance-level explanations for each prediction. This is important in understanding why a particular prediction turned out the way it did. This is not only important for understanding the model; many times, this is needed for compliance purposes as well. I am sure you have seen explanations generated or offered if you are denied credit. The ability to generate these explanations is not straightforward and can be very time-consuming. Let's first look at the explanations generated for the XGBoost model, as shown in the following screenshot:

Figure 7.19 – Model explanations

Since we selected the SHAP option for this project, the model explanations are based on SHapley Additive exPlanations (SHAP) algorithms. Here, you can see the overall distribution of predictions on the left, and you can see that most of the dataset lies in the range of 0 to 10000...