Book Image

Hyperparameter Tuning with Python

By : Louis Owen
Book Image

Hyperparameter Tuning with Python

By: Louis Owen

Overview of this book

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.
Table of Contents (19 chapters)
Section 1:The Methods
Section 2:The Implementation
Section 3:Putting Things into Practice

Exploring CatBoost hyperparameters

Categorical Boosting (CatBoost) is another boosting algorithm built on top of a collection of decision trees, similar to XGBoost and LightGBM. It can also be utilized both for classification and regression tasks. The main difference between CatBoost and XGBoost or LightGBM is how it grows the trees. In XGBoost and LightGBM, trees are grown asymmetrically, while in CatBoost, trees are grown symmetrically so that all of the trees are balanced. This balanced tree characteristic provides several benefits, including the ability to control overfitting problems, lower inference time, and efficient implementation in CPUs. CatBoost does this by using the same condition in every split in the nodes, as shown in the following diagram:

Figure 11.3 – Asymmetric versus symmetric tree

The main selling point of CatBoost is its ability to handle numerous types of features automatically, including numerical, categorical, and text, especially...