If you want to master building classification systems for machine learning, you need to understand how to evaluate classifiers. And in turn, that means you need to understand classification metrics. In classification there are a wide variety of metrics, like precision, recall, sensitivity, accuracy, and many others, but most of these metrics are actually based … Read more
Data science is now, and will continue to be a hot job. Right now, the average data science salary is somewhere around $150,000. Although many people are claiming that AI will destroy data science jobs, I’m fairly confident that AI will only increase the need for data scientists. Why? First of all, AI makes it … Read more
This tutorial will show you how to plot an ROC curve in Python using the Seaborn Objects visualization package. The tutorial is divided into sections for easy navigation, so if you need anything specific, just click on the appropriate link below. Table of Contents: Quick Review of ROC Curves Setup Create Data Plot the ROC … Read more
When you begin immersing yourself in the world of classification systems, you’ll encounter a large number of different classification metrics: precision; recall; accuracy; sensitivity and specificity; F1-score; and many more. But as you begin digging into these metrics you’ll find another quantity that underpins them all: true positives. To understand classification generally as well as … Read more
This tutorial will show you how to use the Scikit Learn roc_curve function. It will explain the syntax of the function and show an example of how to use it. The tutorial is organized into sections, so if you need something specific, you can click on any of the following links. Table of Contents: Introduction … Read more
In machine learning, evaluating the performance of a model is as important as its creation. We need tools and techniques to help guarantee that the model performs well and meets the standards of our task. Enter the ROC curve – a powerful visualization designed for evaluating the performance of a machine learning classification system. This … Read more
The confusion matrix is an important and commonly used tool in machine learning. This is particularly true of classification problems, where we build systems that predict categorical values. Because they’re used so frequently for classification problems, you need to know them, and you need to know them well. So in this blog post, I’m going … Read more
In this tutorial, I’ll show you how to use the Sklearn Logistic Regression function to create logistic regression models in Python. I’ll quickly review what logistic regression is, explain the syntax of Sklearn LogisticRegression, and I’ll show you a step-by-step example of how to use the technique. If you need something specific, just click on … Read more
With the rise of AI, machine learning has suddenly become very popular. Machine learning has been around for decades, but machine learning systems are becoming increasingly important in a range of fields, from healthcare, to finance, to marketing. Python, with a range of libraries for data science and ML, has arguably become the top language … Read more
The Perceptron stands as one of the most basic building blocks for creating neural networks, including more advanced structures like deep networks and their variants. Originally developed in the late 1950s, Perceptrons were designed to mimic the function of biological neurons. I recently wrote a blog post explaining some of the history of Perceptrons as … Read more