You're reading from Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Length 618 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Prateek Joshi

Alberto Artasanchez

View More author details

Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER

3. Machine Learning Pipelines

4. Feature Selection and Feature Engineering

5. Classification and Regression Using Supervised Learning

6. Predictive Analytics with Ensemble Learning

7. Detecting Patterns with Unsupervised Learning

8. Building Recommender Systems

9. Logic Programming

10. Heuristic Search Techniques

11. Genetic Algorithms and Genetic Programming

12. Artificial Intelligence on the Cloud

13. Building Games with Artificial Intelligence

14. Building a Speech Recognizer

15. Natural Language Processing

16. Chatbots

17. Sequential Data and Time Series Analysis

18. Image Recognition

19. Neural Networks

20. Deep Learning with Convolutional Neural Networks

21. Recurrent Neural Networks and Other Deep Learning Models

22. Creating Intelligent Agents with Reinforcement Learning

23. Artificial Intelligence and Big Data

24. Other Books You May Enjoy

25. Index

Confusion matrixes

A confusion matrix is a figure or a table that is used to describe the performance of a classifier. Each row in the matrix represents the instances in a predicted class and each column represents the instances in an actual class. This name is used because the matrix makes it easy to visualize if the model confused or mislabeled two classes. We compare each class with every other class and see how many samples are classified correctly or misclassified.

During the construction of this table, we come across several key metrics that are important in the field of machine learning. Let's consider a binary classification case where the output is either 0 or 1:

True positives: These are the samples for which we predicted 1 as the output and the ground truth is 1 too.
True negatives: These are the samples for which we predicted 0 as the output and the ground truth is 0 too.
False positives: These are the samples for which we predicted...