Book Image

Applied Machine Learning Explainability Techniques

By : Aditya Bhattacharya
Book Image

Applied Machine Learning Explainability Techniques

By: Aditya Bhattacharya

Overview of this book

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.
Table of Contents (16 chapters)
1
Section 1 – Conceptual Exposure
5
Section 2 – Practical Problem Solving
12
Section 3 –Taking XAI to the Next Level

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "For this example, we will use the RegressionExplainer and ExplainerDashboard submodules."

A block of code is set as follows:

pdp = PartialDependence(
    predict_fn=model.predict_proba,
    data=x_train.astype('float').values,
    feature_names=list(x_train.columns),
    feature_types=feature_types)
pdp_global=pdp.explain_global(name='Partial Dependence')

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

explainer = shap.Explainer(model, x_test)
shap_values = explainer(x_test)
shap.plots.waterfall(shap_values[0], max_display = 12,
                     show=False)

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Due to these known drawbacks, the search for a robust Explainable AI (XAI) framework is still on."

Tips or important notes

Appear like this.