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Perform Accessible Machine Learning and Extreme Gradient Boosting with Python

Ken Simmons

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Published in Hands On Gradient Boosting With XGBoost And Scikit Learn: Perform Accessible Machine Learning And Extreme Gradient Boosting With Python

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Machine Learning and Extreme Gradient Boosting
Python for Machine Learning
Getting Started
Handling Missing Values
Feature Engineering
Model Evaluation
Extreme Gradient Boosting
Real-World Applications

Machine learning and extreme gradient boosting are powerful techniques that can be used to solve a wide range of problems in data analysis. However, these techniques can be complex and difficult to implement, especially for beginners. This book provides a comprehensive and accessible to machine learning and extreme gradient boosting with Python.

Machine Learning and Extreme Gradient Boosting

Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed. Extreme gradient boosting is a powerful machine learning algorithm that can be used for a variety of tasks, such as classification, regression, and ranking.

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

by Corey Wade

4.6 out of 5

Language	:	English
File size	:	9466 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	310 pages

Python for Machine Learning

Python is a popular programming language for machine learning. It is easy to learn and use, and it has a large number of libraries and tools for machine learning.

Getting Started

To get started with machine learning and extreme gradient boosting, you will need to install Python and a few libraries. You can find instructions on how to do this in the book.

Handling Missing Values

Missing values are a common problem in data analysis. There are a number of ways to handle missing values, such as ignoring them, imputing them, or deleting them. The best way to handle missing values depends on the dataset and the task at hand.

Feature Engineering

Feature engineering is the process of transforming raw data into features that are more useful for machine learning. Feature engineering can improve the performance of machine learning models and make them more interpretable.

Model Evaluation

Model evaluation is the process of assessing the performance of a machine learning model. There are a number of different metrics that can be used to evaluate machine learning models, such as accuracy, precision, recall, and F1 score.

Extreme Gradient Boosting

Extreme gradient boosting is a powerful machine learning algorithm that can be used for a variety of tasks, such as classification, regression, and ranking. Extreme gradient boosting is an ensemble method, which means that it combines the predictions of multiple weak learners to create a more accurate model.

Real-World Applications

Machine learning and extreme gradient boosting are used in a wide range of real-world applications, such as:

Predicting customer churn
Detecting fraud
Recommending products
Classifying images
Predicting weather

Machine learning and extreme gradient boosting are powerful techniques that can be used to solve a wide range of problems in data analysis. This book provides a comprehensive and accessible to machine learning and extreme gradient boosting with Python. By the end of this book, you will be able to build and evaluate machine learning models to solve real-world problems.

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

by Corey Wade

4.6 out of 5