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Practical Machine Learning Cookbook

You're reading from   Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785280511
Length 570 pages
Edition 1st Edition
Languages
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Author (1):
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Atul Tripathi Atul Tripathi
Author Profile Icon Atul Tripathi
Atul Tripathi
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Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Classification 3. Clustering 4. Model Selection and Regularization 5. Nonlinearity 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement Learning 9. Structured Prediction 10. Neural Networks 11. Deep Learning 12. Case Study - Exploring World Bank Data 13. Case Study - Pricing Reinsurance Contracts 14. Case Study - Forecast of Electricity Consumption

Smoothing splines - understanding cars and speed

In order to determine the parameters required in statistics for fitting a model, multiple methods can be used. In each of the cases, fitting involves the estimating of a small number of parameters from the data. Apart from estimating parameters, two important stages are the identification of a suitable model and the verification of the model. These smoothing methods can be used in a variety of ways: to aid understanding and produce smoothed plots, to identify a suitable parametric model from the shape of the smoothed data, or to focus on the effects of interest in order to eliminate complex effects which are of no use.

How to do it...

Let's get into the details.

Step 1 - exploring the data

The first step is to load the following packages:

> install.packages("graphics")
> install.packages("splines")
> library(graphics)
> library(splines)

Creating a matrix. The cbind() function takes the sequence of...

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