If you’ve worked in AI and machine learning, you probably know that getting good training data is one of the core problems with building good models. And in many cases, we need a lot of training data to build models that generalize well, meaning, models that perform well both during training, but also on new … Read more
Dealing with the problem of overfitting is one of the core issues in machine learning and AI. Your model seems to work perfectly on the training set, but when you try to validate it on the test set … it’s terrible. This is a core problem in machine learning, especially when you start using advanced … Read more
If you want to build high-performing machine learning and AI systems, then simply training those systems is rarely enough. You often need to build multiple models, often with multiple different algorithms, and then compare the different models to each other to see which is best. And further, you often need to “tune” the settings of … Read more
In machine learning, making sure that you have a model that performs well is, in some sense, the most important thing. This means that you need to be really good at evaluating different models. But, this can be a challenge, as you run into issues like overfitting, hyperparameter optimization, and model selection. One way to … Read more
Mmm. Overfitting. It’s the bane of most machine learning developers. You build a model that performs so well on the training data, and think “I’ve done such a good job!” while patting yourself on the back. Then, you test the model later, only to find that it performs terribly. Welcome to overfitting. Overfitting is probably … Read more
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