optuna

You’ve built a CatBoost model; now what? Hyperparameter tuning is the key to unlocking your model’s full potential. But if the thought of tackling this task feels daunting, you’re not alone. Once you’ve mastered the tips and tricks presented in this tutorial, you’ll be equipped with the skills to fine-tune any CatBoost model effectively. Let’s get started! Installing CatBoost and Optuna First, let’s install both libraries simply by running: pip install catboost optuna Or, if you’re using Anaconda, run:...

Trying to find the right hyperparameters for XGBoost can feel like searching for a needle in a haystack. Trust me, I’ve been there. XGBoost was a crucial model to win at least two of the Kaggle competitions I participated in. By the end of this tutorial, you’ll be equipped with the exact same techniques I used to optimize my models and achieve those top rankings. Let’s get started! Installing XGBoost And Optuna Installing XGBoost is easy, just run:...

As a Kaggle Grandmaster, I absolutely love working with LightGBM, a fantastic machine learning library that’s become one of my go-to tools. I always focus on tuning the model’s hyperparameters before diving into feature engineering. Think of it like cooking up the perfect dish. You want to make sure you’ve got the right ingredients and their quantities before you start experimenting with new flavors. By fine-tuning your hyperparameters first, you’ll squeeze every last drop of performance from your model in the data you already have....

In this article, we will explore the use of Gaussian Processes for time series forecasting in Python, specifically using the GluonTS library. GluonTS is an open-source toolkit for building and evaluating state-of-the-art time series models. One of the key benefits of using Gaussian Processes for time series forecasting is that they can provide probabilistic predictions. Instead of just predicting a point estimate for the next value in the time series, GPs can provide a distribution over possible values, allowing us to quantify our uncertainty....

Today, we’re going to explore multiple time series forecasting with LightGBM in Python. If you’re not already familiar, LightGBM is a powerful open-source gradient boosting framework that’s designed for efficiency and high performance. It’s a great tool for tackling large datasets and can help you create accurate predictions in a flash. When combined with the MLForecast library, it becomes a versatile and scalable solution for multiple time series forecasting. Let’s dive into the step-by-step process of preparing our data, defining our LightGBM model, and training it using MLForecast in Python....