Pandas Melt, Explained

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In this blog post, I’ll show you how to use the Pandas melt method. I explain what melt does, how the syntax works, and I’ll show you a simple example. If you need something specific, you can click on any of the following links: Table of Contents: Introduction Syntax Examples Frequently Asked Questions Having said … Read more

Classifier Precision, Explained

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An image that shows a classifier classifying examples with positive and negative predictions, and then an accompanying equation that visualizes classification precision as true positives divided by true positives plus false positives.

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

Sklearn confusion_matrix, Explained

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An image of a confusion matrix, similar to the type of confusion matrix that we would make with Sklearn confusion_matrix.

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

Classification Accuracy, Explained

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An image of an arrow hitting a bullseye, and the equation for classification accuracy.

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

False Negative: What they are, Why They’re Bad, and 7 Ways to Fix Them

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An image that shows False Negatives, and how they're related to classification threshold, TP, TN, and FP.

When you’re working with classification and detection systems, you’ll commonly hear the term “False Negative.” You might be asking, what is a False Negative? And if you’re a serious machine learning practitioner, how do you fix them? Well, if you’re asking yourself these questions, you’re in luck. In this tutorial, I’m going to explain all … Read more

The 5 Python Skills You Need Before You Study Machine Learning

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And image showing the 5 Python skills you need before you study machine learning.

Machine learning is a very powerful skill. And machine learning is a valuable skill. According to Glassdoor, the average salary for a machine learning engineer is about $140,000 in 2023. Mastering machine learning could increase your earning power a lot. The question is, how? What’s the best way to get started with machine learning? What … Read more

7 Secrets To Help You Master Python Data Science

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If you’ve been reading the Sharp Sight blog for a while, you’ll know that I’m a big fan of elite performers: Navy SEALs, elite athletes, grandmaster chess players, etcetera. I like to research how elite performers operate, because I want to find what makes them special. The techniques. The training methods. The mindsets. In particular, … Read more

False Positive, Explained

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An image of False Positives, that also shows the relation of FPs to TPs, FNs, TNs, and classification threshold.

Have you ever had someone talk about a classification system or medical diagnostics and mention a “False Positive?” It’s ok … False positives can be confusing if you haven’t worked with classifiers and detection systems before. But they’re important … Which is why, in this blog post, I’m going to explain almost everything you need … Read more