Welcome to our deep dive into one of the foundations of machine learning: Training, Validation, and Test Sets. In this blog post, I’ll explain the purpose of having these different machine learning datasets, explaining their roles, and discuss a few of the main strategies for data splitting. If you need something specific, just click on … Read more
If you want to master machine learning and AI, you’ll need to learn and master a variety of minor concepts that underpin these systems. One such concept is the classification threshold. The classification threshold is critical for a large number of machine learning algorithms – from logistic regression to neural network classification – and it’s … Read more
In this tutorial, I’ll show you how to plot a precision-recall curve in Python using Seaborn. Specifically, I’ll show you how to use the new Seaborn Objects package to plot it. The article is divided into several different sections to make it easier to navigate. So if you need something specific, just click on any … Read more
Binary classification stands as a fundamental concept of machine learning, serving as the cornerstone for many predictive modeling tasks. At its core, binary classification involves categorizing data into two distinct groups based on specific criteria, a process akin to making a ‘yes or no’ decision. This simplicity conceals its broad usefulness, in tasks ranging from … Read more
In this blog post, I’m going to quickly explain positive and negative classes in machine learning classification. I’ll explain what the positive and negative classes are, how they relate to classification metric, some examples of positive and negative in real-world machine learning, and more. If you need something specific, just click on one of these … Read more
If you want to master modern machine learning and AI, one of the major sub-areas that you need to master is classification. Classification is one of the most important types of task in machine learning and AI. But mastering classification, in part, means mastering how to evaluate classification systems. Which in turn, means understanding the … Read more
Machine learning – and the related field of AI – will probably be worth millions of dollars for people who master these skills. But as I always tell my students: to master ML and AI, you need to master the basics. And part of “the basics” are evaluation metrics … … like classifier recall. This … Read more
This blog post explains precision in classifiers and machine learning models. It will explain what precision is, the pros and cons of this metric, how to improve precision, and more. Table of Contents: A Quick Review of Classification Precision Basics Pros and Cons of Precision How to Improve Precision Alternatives to Precision If there’s something … Read more
Scikit-learn, which is affectionately known as sklearn among Python data scientists, is a Python library that offers a wide range of machine learning tools. Among these tools is the confusion_matrix function, which is indispensable when working on classification problems. So in this tutorial, I’ll show you how to use the sklearn confusion_matrix function. I’ll give … Read more
This blog post will explain classification accuracy. It will explain what accuracy is, the pros and cons of this metric, how to improve accuracy, and more. Table of Contents: A Quick Review of Classification Classification Accuracy Basics Pros and Cons of Accuracy How to Improve Accuracy Alternatives to Accuracy If you need something specific, you … Read more