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 LightGBM hyperparameters

Light Gradient Boosting Machine (LightGBM) is also a boosting algorithm built on top of a collection of decision trees, similar to XGBoost. It can also be utilized both for classification and regression tasks. However, it differs from XGBoost in the way the trees are grown. In LightGBM, trees are grown in a leaf-wise manner, while XGBoost grows trees in a level-wise manner (see Figure 11.2). By leaf-wise, we mean that LightGBM grows trees by prioritizing nodes whose split leads to the highest increase of homogeneity:

Figure 11.2 – Level-wise versus leaf-wise tree growth

Besides the difference in how XGBoost and LightGBM grow the trees, they also have different ways of handling categorical features. In XGBoost, we need to encode the categorical features before passing them to the model. This is usually done using the one-hot encoding or integer encoding methods. In LightGBM, we can just tell which features are categorical...