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Debugging Machine Learning Models with Python

Debugging Machine Learning Models with Python

By : Ali Madani
4.9 (16)
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Debugging Machine Learning Models with Python

Debugging Machine Learning Models with Python

4.9 (16)
By: Ali Madani

Overview of this book

Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.
Table of Contents (26 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share your thoughts
Download a free PDF copy of this book
1
Part 1:Debugging for Machine Learning Modeling
Part 1:Debugging for Machine Learning Modeling
Free Chapter
2
Chapter 1: Beyond Code Debugging
Chapter 1: Beyond Code Debugging
Technical requirements
Machine learning at a glance
Types of machine learning modeling
Debugging in software development
Flaws in data used for modeling
Model and prediction-centric debugging
Summary
Questions
References
3
Chapter 2: Machine Learning Life Cycle
Chapter 2: Machine Learning Life Cycle
Technical requirements
Before we start modeling
Data collection
Data selection
Data exploration
Data wrangling
Modeling data preparation
Model training and evaluation
Testing the code and the model
Model deployment and monitoring
Summary
Questions
References
4
Chapter 3: Debugging toward Responsible AI
Chapter 3: Debugging toward Responsible AI
Technical requirements
Impartial modeling fairness in machine learning
Security and privacy in machine learning
Transparency in machine learning modeling
Accountable and open to inspection modeling
Data and model governance
Summary
Questions
References
5
Part 2:Improving Machine Learning Models
Part 2:Improving Machine Learning Models
6
Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models
Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models
Technical requirements
Performance and error assessment measures
Visualization for performance assessment
Bias and variance diagnosis
Model validation strategy
Error analysis
Beyond performance
Summary
Questions
References
7
Chapter 5: Improving the Performance of Machine Learning Models
Chapter 5: Improving the Performance of Machine Learning Models
Technical requirements
Options for improving model performance
Synthetic data generation
Improving pre-training data processing
Regularization to improve model generalizability
Summary
Questions
References
8
Chapter 6: Interpretability and Explainability in Machine Learning Modeling
Chapter 6: Interpretability and Explainability in Machine Learning Modeling
Technical requirements
Interpretable versus black-box machine learning
Explainability methods in machine learning
Practicing machine learning explainability in Python
Reviewing why having explainability is not enough
Summary
Questions
References
9
Chapter 7: Decreasing Bias and Achieving Fairness
Chapter 7: Decreasing Bias and Achieving Fairness
Technical requirements
Fairness in machine learning modeling
Sources of bias
Using explainability techniques
Fairness assessment and improvement in Python
Summary
Questions
References
10
Part 3:Low-Bug Machine Learning Development and Deployment
Part 3:Low-Bug Machine Learning Development and Deployment
11
Chapter 8: Controlling Risks Using Test-Driven Development
Chapter 8: Controlling Risks Using Test-Driven Development
Technical requirements
Test-driven development for machine learning modeling
Machine learning differential testing
Tracking machine learning experiments
Summary
Questions
References
12
Chapter 9: Testing and Debugging for Production
Chapter 9: Testing and Debugging for Production
Technical requirements
Infrastructure testing
Integration testing of machine learning pipelines
Monitoring and validating live performance
Model assertion
Summary
Questions
References
13
Chapter 10: Versioning and Reproducible Machine Learning Modeling
Chapter 10: Versioning and Reproducible Machine Learning Modeling
Technical requirements
Reproducibility in machine learning
Data versioning
Model versioning
Summary
Questions
References
14
Chapter 11: Avoiding and Detecting Data and Concept Drifts
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Chapter 11: Avoiding and Detecting Data and Concept Drifts
Technical requirements
Avoiding drifts in your models
Detecting drifts
Summary
Questions
References
15
Part 4:Deep Learning Modeling
Part 4:Deep Learning Modeling
16
Chapter 12: Going Beyond ML Debugging with Deep Learning
Chapter 12: Going Beyond ML Debugging with Deep Learning
Technical requirements
Introduction to artificial neural networks
Frameworks for neural network modeling
Summary
Questions
References
17
Chapter 13: Advanced Deep Learning Techniques
Chapter 13: Advanced Deep Learning Techniques
Technical requirements
Types of neural networks
Convolutional neural networks for image shape data
Transformers for language modeling
Modeling graphs using deep neural networks
Summary
Questions
References
18
Chapter 14: Introduction to Recent Advancements in Machine Learning
Chapter 14: Introduction to Recent Advancements in Machine Learning
Technical requirements
Generative modeling
Reinforcement learning
Self-supervised learning (SSL)
Summary
Questions
References
19
Part 5:Advanced Topics in Model Debugging
Part 5:Advanced Topics in Model Debugging
20
Chapter 15: Correlation versus Causality
Chapter 15: Correlation versus Causality
Technical requirements
Correlation as part of machine learning models
Causal modeling to reduce risks and improve performance
Assessing causation in machine learning models
Causal modeling using Python
Summary
Questions
References
21
Chapter 16: Security and Privacy in Machine Learning
Chapter 16: Security and Privacy in Machine Learning
Technical requirements
Encryption techniques and their use in machine learning
Homomorphic encryption
Differential privacy
Federated learning
Summary
Questions
References
22
Chapter 17: Human-in-the-Loop Machine Learning
Chapter 17: Human-in-the-Loop Machine Learning
Humans in the machine learning life cycle
Human-in-the-loop modeling
Summary
Questions
References
23
Assessments
Chapter 1 – Beyond Code Debugging
Chapter 2 – Machine Learning Life Cycle
Chapter 3 – Debugging toward Responsible AI
Chapter 4 – Detecting Performance and Efficiency Issues in Machine Learning Models
Chapter 5 – Improving the Performance of Machine Learning Models
Chapter 6 – Interpretability and Explainability in Machine Learning Modeling
Chapter 7 – Decreasing Bias and Achieving Fairness
Chapter 8 – Controlling Risks Using Test-Driven Development
Chapter 9 – Testing and Debugging for Production
Chapter 10 – Versioning and Reproducible Machine Learning Modeling
Chapter 11 – Avoiding and Detecting Data and Concept Drifts
Chapter 12 – Going Beyond ML Debugging with Deep Learning
Chapter 13 – Advanced Deep Learning Techniques
Chapter 14 – Introduction to Recent Advancements in Machine Learning
Chapter 15 – Correlation versus Causality
Chapter 16 – Security and Privacy in Machine Learning
Chapter 17 – Human-in-the-Loop Machine Learning
24
Index
Index
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25
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Detecting drifts

Avoiding drifts altogether in all our models is not possible, but we can aim to detect them early on and eliminate them. Here, we are going to practice drift detection with alibi_detect and evidently in Python.

Practicing with alibi_detect for drift detection

One of the widely-used Python libraries for drift detection that we want to practice with is alibi_detect. We will first import the necessary Python functions and classes and generate a synthetic dataset with 10 features and 10,000 samples using make_classification from scikit-learn:

import numpy as npimport pandas as pd
import lightgbm as lgb
from alibi_detect.cd import KSDrift
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score as bacc
# Generate synthetic data
X, y = make_classification(n_samples=10000, n_features=10,
    n_classes=2, random_state=42)

Then, we split the data...

End of Section 4
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