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  • Book Overview & Buying R Machine Learning Essentials
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R Machine Learning Essentials

R Machine Learning Essentials

By : Usuelli
5 (1)
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R Machine Learning Essentials

R Machine Learning Essentials

5 (1)
By: Usuelli
Buy this Book

Overview of this book

If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology.  They have to do with people.  People didn’t like the new technology.  People weren’t trained properly on the change.  People hadn’t received adequate communications and didn’t understand the change.  Sound familiar?
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Transforming Data into Actions
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1. Transforming Data into Actions
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A data-driven approach in business decisions
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Identifying hidden patterns
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Estimating the impact of an action
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Summary
2
2. R – A Powerful Tool for Developing Machine Learning Algorithms
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2. R – A Powerful Tool for Developing Machine Learning Algorithms
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Why R
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The R tutorial
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Some useful R packages
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Summary
3
3. A Simple Machine Learning Analysis
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3. A Simple Machine Learning Analysis
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Exploring data interactively
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Exploring the data using machine learning models
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Predicting newer outcomes
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Summary
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4. Step 1 – Data Exploration and Feature Engineering
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4. Step 1 – Data Exploration and Feature Engineering
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Building a machine learning solution
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Building the feature data
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Exploring and visualizing the features
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Modifying the features
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Ranking the features using a filter or a dimensionality reduction
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Summary
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5. Step 2 – Applying Machine Learning Techniques
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5. Step 2 – Applying Machine Learning Techniques
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Identifying a homogeneous group of items
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Applying the k-nearest neighbor algorithm
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Optimizing the k-nearest neighbor algorithm
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Summary
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6. Step 3 – Validating the Results
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6. Step 3 – Validating the Results
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Validating a machine learning model
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Tuning the parameters
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Selecting the data features to include in the model
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Tuning features and parameters together
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Summary
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7. Overview of Machine Learning Techniques
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7. Overview of Machine Learning Techniques
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Overview
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Supervised learning
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Linear regression
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Perceptron
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Unsupervised learning
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Summary
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8. Machine Learning Examples Applicable to Businesses
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8. Machine Learning Examples Applicable to Businesses
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Overview of the problem
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Clustering the clients
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Predicting the output
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Summary
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Index
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Index

Building a machine learning solution

In which stage of a machine learning solution are we defining the features? Let's look at an overview of the whole procedure of building the solution. We can divide the approach into three steps:

  1. Define the features that we will be using.

  2. Apply one or more techniques to solve the problem.

  3. Evaluate the result and optimize the performance.

In the first step, we can evaluate the relevance of each feature by using a filter and selecting the most relevant feature. We can also define a combination of some features that are good for describing the data.

In the second step, when we build a model, we can use some techniques (embedded methods) that rank the features and identify the most relevant feature automatically.

The last step is very important since we have more information, allowing us to identify a more proper feature set. For instance, we can use the same model with different sets of features and evaluate which feature combination performs better. An option...

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Tech Concepts
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Programming languages
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R Machine Learning Essentials
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