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Statistics for Machine Learning
Statistics for Machine Learning

Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Profile Icon Pratap Dangeti
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (6 Ratings)
Paperback Jul 2017 442 pages 1st Edition
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$29.99 $43.99
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Arrow left icon
Profile Icon Pratap Dangeti
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (6 Ratings)
Paperback Jul 2017 442 pages 1st Edition
eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$29.99 $43.99
Paperback
$54.99
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Renews at $19.99p/m

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Statistics for Machine Learning

Parallelism of Statistics and Machine Learning

At first glance, machine learning seems to be distant from statistics. However, if we take a deeper look into them, we can draw parallels between both. In this chapter, we will deep dive into the details. Comparisons have been made between linear regression and lasso/ridge regression in order to provide a simple comparison between statistical modeling and machine learning. These are basic models in both worlds and are good to start with.

In this chapter, we will cover the following:

  • Understanding of statistical parameters and diagnostics
  • Compensating factors in machine learning models to equate statistical diagnostics
  • Ridge and lasso regression
  • Comparison of adjusted R-square with accuracy

Comparison between regression and machine learning models

Linear regression and machine learning models both try to solve the same problem in different ways. In the following simple example of a two-variable equation fitting the best possible plane, regression models try to fit the best possible hyperplane by minimizing the errors between the hyperplane and actual observations. However, in machine learning, the same problem has been converted into an optimization problem in which errors are modeled in squared form to minimize errors by altering the weights.

In statistical modeling, samples are drawn from the population and the model will be fitted on sampled data. However, in machine learning, even small numbers such as 30 observations would be good enough to update the weights at the end of each iteration; in a few cases, such as online learning, the model will be updated with...

Compensating factors in machine learning models

Compensating factors in machine learning models to equate statistical diagnostics is explained with the example of a beam being supported by two supports. If one of the supports doesn't exist, the beam will eventually fall down by moving out of balance. A similar analogy is applied for comparing statistical modeling and machine learning methodologies here.

The two-point validation is performed on the statistical modeling methodology on training data using overall model accuracy and individual parameters significance test. Due to the fact that either linear or logistic regression has less variance by shape of the model itself, hence there would be very little chance of it working worse on unseen data. Hence, during deployment, these models do not incur too many deviated results.

However, in the machine learning space, models...

Machine learning models - ridge and lasso regression

In linear regression, only the residual sum of squares (RSS) is minimized, whereas in ridge and lasso regression, a penalty is applied (also known as shrinkage penalty) on coefficient values to regularize the coefficients with the tuning parameter λ.

When λ=0, the penalty has no impact, ridge/lasso produces the same result as linear regression, whereas λ -> ∞ will bring coefficients to zero:

Before we go deeper into ridge and lasso, it is worth understanding some concepts on Lagrangian multipliers. One can show the preceding objective function in the following format, where the objective is just RSS subjected to cost constraint (s) of budget. For every value of λ, there is an s such that will provide the equivalent equations, as shown for the overall objective function with a...

Summary

In this chapter, you have learned the comparison of statistical models with machine learning models applied on regression problems. The multiple linear regression methodology has been illustrated with a step-by-step iterative process using the statsmodel package by removing insignificant and multi-collinear variables. Whereas, in machine learning models, removal of variables does not need to be removed and weights get adjusted automatically, but have parameters which can be tuned to fine-tune the model fit, as machine learning models learn by themselves based on data rather than exclusively being modeled by removing variables manually. Though we got almost the same accuracy results between linear regression and lasso/ridge regression methodologies, by using highly powerful machine learning models such as random forest, we can achieve much better uplift in model accuracy...

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Key benefits

  • Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
  • Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
  • Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.

Description

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.

Who is this book for?

This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.

What you will learn

  • Understand the statistical and machine learning fundamentals necessary to
  • build models
  • Understand the major differences and parallels between the statistical way and the machine learning way to solve problems
  • Learn how to prepare data and feed models by using the appropriate machine learning algorithms from the more-than-adequate R and Python packages
  • Analyze the results and tune the model appropriately to your own predictive goals
  • Understand the concepts of the statistics required for machine learning
  • Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models
  • Learn reinforcement learning and its application in the field of artificial intelligence domain

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 21, 2017
Length: 442 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295758
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Product Details

Publication date : Jul 21, 2017
Length: 442 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295758
Category :
Languages :

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Table of Contents

9 Chapters
Journey from Statistics to Machine Learning Chevron down icon Chevron up icon
Parallelism of Statistics and Machine Learning Chevron down icon Chevron up icon
Logistic Regression Versus Random Forest Chevron down icon Chevron up icon
Tree-Based Machine Learning Models Chevron down icon Chevron up icon
K-Nearest Neighbors and Naive Bayes Chevron down icon Chevron up icon
Support Vector Machines and Neural Networks Chevron down icon Chevron up icon
Recommendation Engines Chevron down icon Chevron up icon
Unsupervised Learning Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
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1 star 16.7%
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Amazon Customer Nov 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Useful
Amazon Verified review Amazon
Enrico P. Apr 13, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Un ottimo libro per chi vuole approfondire la parte statistica del Machine Learning (molto approfondito) e del Deep Learning (in maniera superficiale) ricco di esempi e di codice in Python e R; adatto per chi è ad una seconda fase di approfondimento delle stesse tematiche.
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David Oct 22, 2017
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Just finished this book as a primer for my machine learning course this week. It is an excellent resource, that has both Python and R examples throughout the text. Some examples are only in Python when R has no library or functionality for the example. It is easy to read and a determined student could read it in about 4 hours. The examples used are great at illustrating the different algorithms. Great book and glad I was able to find it this past week before I dive back into the classroom.
Amazon Verified review Amazon
Mark Richmond Jun 02, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I find this book to be incredibly frustrating. It's a good book on the whole, a lot of very good and useful information is presented and the author clearly knows what he's talking about. However, the editing is unbelievably bad. All the figures and even the equations are poor quality, they would not be accepted in a journal so why is this quality of figure ok in a book? The figures are so bad that it's often hard to see what the author is even talking about. The author sometimes mentions the colours in a figure, but all the figures are black and white! The author clearly isn't a native English speaker, this is fine of course, but someone who is really should have corrected this before it was published. There are so many sentences which simply don't make sense. Some of the topics are hard enough to understand without having to decipher what sentences are supposed to say.On the whole, now that I have the book I think it's worth persisting to get the value from it because there's a lot of good stuff in here. But I find it hard to recommend that anyone buy this without these issues being fixed.
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rajdeep banerjee Sep 26, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Pros:Concise and to the point with panda and R codes.Cons:Needs some more mathematical details for better understanding.
Amazon Verified review Amazon
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