Are you trying to create a regression model using the LightGBM library in Python but finding it challenging? Perhaps you’re unsure about installing the library, setting up the model, preparing the data, or evaluating your model’s performance. You’re in the right place. This tutorial will guide you through each of these steps. We’ll install LightGBM, prepare a dataset, train a model, make predictions, and evaluate the results. By the end, you’ll have a functional LightGBM regression model and a solid understanding of the process....

Gradient boosted decision trees (GBDTs) are the current state of the art on tabular data. They are used in many Kaggle competitions and are the go-to model for many data scientists, as they tend to get better performance than neural networks while being easier and faster to train. Neural networks, on the other hand, are the state of the art in many other tasks, such as image classification, natural language processing, and speech recognition....

Want to use LightGBM for a binary classification task but feel stuck? Maybe you’re not quite sure how to install the library, load your data, or even how to handle an imbalance in your classes. In this tutorial, we’re going to tackle these problems head-on. We’ll start by learning how to install LightGBM in your Python environment. Next, I’ll guide you through the process of loading and preparing your data for the model....

As a Python user aiming to predict a continuous target variable from a dataset with both numerical and categorical features, you’ve made a great choice in considering CatBoost. This high-performance machine learning algorithm is particularly known for its ability to handle categorical variables effectively. In this tutorial, I’ll guide you step-by-step on how to use CatBoost for regression tasks. We’ll start from preparing your data, training the CatBoost model, and finally evaluating its performance....

Are you looking to tackle a multiclass classification problem using Python and stumbled upon CatBoost? Or perhaps you’ve heard about CatBoost’s impressive handling of categorical data and now you’re curious to see it in action with multiclass classification. Either way, you’ve come to the right place! In this tutorial, we’re going to explore how to use CatBoost, a powerful machine learning library, to conquer multiclass classification problems. I’ll start by giving you a quick primer on CatBoost and why it’s an excellent choice for multiclass classification....

Have you ever found yourself puzzled by the different options for categorical encoding in CatBoost? With so many methods available, it can be quite a challenge to figure out which one is the best fit for your project. In this tutorial, I will demystify the various encoding options. By the end of this guide, you’ll be well-equipped to make an informed decision and handle categorical features in CatBoost like a pro!...

Many people find the initial setup of CatBoost a bit daunting. Perhaps you’ve heard about its ability to work with categorical features without any preprocessing, but you’re feeling stuck on how to take the first step. In this step-by-step tutorial, I’m going to simplify things for you. After all, it’s just another gradient boosting library to have in your toolbox. We’ll walk you through the process of installing CatBoost, loading your data, and setting up a CatBoost classifier....

If you’ve ever used CatBoost for machine learning, you know it’s a powerful tool. But did you know it has several ways of calculating feature importances? Understanding how these methods work can help you get more out of your models. However, these methods can get a bit complex, and it’s not always clear when to use each one. It’s like trying to choose the right tool from a toolbox when you don’t know what each tool does....

So, you’ve heard about the power of XGBoost for Learning to Rank (LTR) tasks and want to harness it, right? You couldn’t have landed in a better place! XGBoost is a go-to tool for many LTR applications, from predicting click-through rates and powering search engines to enhancing recommender systems. I can vouch for its effectiveness, having used it to build models for ranking freelancers on Upwork. In this tutorial, we’ll unlock the potential of XGBoost for your LTR tasks....