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Machine Learning with R

Machine Learning with R

By : Brett Lantz
4.2 (46)
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Machine Learning with R

Machine Learning with R

4.2 (46)
By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Table of Contents (16 chapters)
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Index

Chapter 6. Forecasting Numeric Data – Regression Methods

Mathematical relationships help us to make sense of many aspects of everyday life. For example, body weight is a function of one's calorie intake; income is often related to education and job experience; and poll numbers help to estimate a presidential candidate's odds of being re-elected.

When such patterns are formulated with numbers, we gain additional clarity. For example, an additional 250 kilocalories consumed daily may result in nearly a kilogram of weight gain per month; each year of job experience may be worth an additional $1,000 in yearly salary; and a president is more likely to be re-elected when the economy is strong. Obviously, these equations do not perfectly fit every situation, but we expect that they are reasonably correct most of the time.

This chapter extends our machine learning toolkit by going beyond the classification methods covered previously and introducing techniques...

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