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Home > Data > Machine Learning > Machine Learning Quick Reference
Machine Learning Quick Reference
Machine Learning Quick Reference

Machine Learning Quick Reference: Quick and essential machine learning hacks for training smart data models

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Machine Learning Quick Reference

Evaluating Kernel Learning

In machine learning, pattern finding is an area that is being explored to the hilt. There are many methods and algorithms that can drive this kind of work and analysis. However, in this chapter, we will try to focus on how kernels are making a significant difference to the whole machine learning outlook. The application of kernel learning doesn't have any boundaries: starting from a simple regression problem to a computer vision classification, it has made its presence felt everywhere. Support vector machine (SVM) is one of those algorithms that happens to make use of kernel learning.

In this chapter, we will be focusing on the following concepts:

  • Concepts of vectors, linear separability, and hyperplanes
  • SVM
  • Kernel tricks
  • Gaussian process
  • Parameter optimization

Introduction to vectors

Before moving on to the core topic, we would like to build a foundation for getting there. Hence, this segment of the chapter is very important. It might look familiar to you and many of you will be cognizant about this. However, going through this channel will set the flow.

A vector is an object that has both a direction and magnitude. It is represented by an arrow and with a coordinate (x, y) in space, as shown in the following plot:

As shown in the preceding diagram, the vector OA has the coordinates (4,3): 

Vector OA= (4,3)

However, it is not sufficient to define a vector just by coordinates—we also need a direction. That means the direction from the x axis.

Magnitude of the vector

...

Linear separability

Linear separability implies that if there are two classes then there will be a point, line, plane, or hyperplane that splits the input features in such a way that all points of one class are in one-half space and the second class is in the other half-space.

For example, here is a case of selling a house based on area and price. We have got a number of data points for that along with the class, which is house Sold/Not Sold:

In the preceding figure, all the N, are the class (event) of Not Sold, which has been derived based on the Price and Area of the house and all the instances of S represent the class of the house getting sold. The number of N and S represent the data points on which the class has been determined.

In the first diagram, N and S are quite close and happen to be more random, hence, it's difficult to have linear separability...

Hyperplanes 

Many of you will have guessed it right. We use hyperplanes when it comes to more than 3D. We will define it using a bit of mathematics.

A linear equation looks like this: y = ax + b has got two variables, x and y, and a y-intercept, which is b. If we rename y as x2 and x as x1, the equation comes out as x2=ax1 + b which implies ax1 - x2 + b=0. If we define 2D vectors as x= (x1,x2) and w=(a,-1) and if we make use of the dot product, then the equation becomes w.x + b = 0. 

Remember, x.y = x1y1 + x2y2.

So, a hyperplane is a set of points that satisfies the preceding equation. But how do we classify with the help of hyperplane?

We define a hypothesis function h:

h(xi) = +1 if w.xi + b ≥ 0

-1 if w.xi + b < 0

This could be equivalent to the following:

h(xi)= sign(w.xi + b) 

It could also be equivalent to the following...

SVM

Now we are ready to understand SVMs. SVM is an algorithm that enables us to make use of it for both classification and regression. Given a set of examples, it builds a model to assign a group of observations into one category and others into a second category. It is a non-probabilistic linear classifier. Training data being linearly separable is the key here. All the observations or training data are a representation of vectors that are mapped into a space and SVM tries to classify them by using a margin that has to be as wide as possible:

Let's say there are two classes A and B as in the preceding screenshot.

And from the preceding section, we have learned the following:

g(x) = w. x + b

Where:

  • w: Weight vector that decides the orientation of the hyperplane
  • b: Bias term that decides the position of the hyperplane in n-dimensional space by biasing it

The...

Kernel trick

We have already seen that SVM works smoothly when it comes to having linear separable data. Just have a look at the following figure; it depicts that vectors are not linearly separable, but the noticeable part is that it is not being separable in 2D space:

With a few adjustments, we can still make use of SVM here.

Transformation of a two-dimensional vector into a 3D vector or any other higher dimensional vector can set things right for us. The next step would be to train the SVM using a higher dimensional vector. But the question arises of how high in dimension we should go to transform the vector. What this means is if the transformation has to be a two-dimensional vector, or 3D or 4D or more. It actually depends on the which brings separability into the dataset.

...

Kernel types

We're going to explain the types of in this section.

Linear kernel

Let's say there are two vectors, x1 and x2, so the linear kernel can be defined by the following:

K(x1, x2)= x1 . x2

Polynomial kernel

If there are two vectors, x1 and x2, the linear kernel can be defined by the following:

K(x1, x2)= (x1 . x2 + c)d

Where:

  • c: Constant
  • d: Degree of polynomial:
def polynomial_kernel(x1, x2, degree, constant=0): 
    result = sum([x1[i] * x2[i] for i in range(len(x1))]) + constant 
    return...

SVM example and parameter optimization through grid search

Here, we are taking a breast cancer dataset wherein we have classified according to whether the cancer is benign/malignant.

The following is for importing all the required libraries:

import pandas as pd
import numpy as np
from sklearn import svm, datasets
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.utils import shuffle
%matplotlib inline

Now, let's load the breast cancer dataset:

BC_Data = datasets.load_breast_cancer()

The following allows us to check the details of the dataset:

print(BC_Data.DESCR)

This if for splitting the dataset into train and test:

X_train, X_test, y_train, y_test = train_test_split(BC_Data.data, BC_Data.target, random_state...

Introduction to vectors

Before moving on to the core topic, we would like to build a foundation for getting there. Hence, this segment of the chapter is very important. It might look familiar to you and many of you will be cognizant about this. However, going through this channel will set the flow.

A vector is an object that has both a direction and magnitude. It is represented by an arrow and with a coordinate (x, y) in space, as shown in the following plot:

As shown in the preceding diagram, the vector OA has the coordinates (4,3): 

Vector OA= (4,3)

However, it is not sufficient to define a vector just by coordinates—we also need a direction. That means the direction from the x axis.

Magnitude of the vector

The magnitude of the vector is also called the norm. It is represented by ||OA||:

To find out magnitude of this vector, we can follow the Pythagorean theorem:

OA2 = OB2 + AB2

= 42 + 32 

= 16 + 9

= 25

Hence:

OA = √25 = 5

||OA||= 5

So, if there is a vector x = (x1,x2,....,xn):

||x||= x12 + x22+........

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Key benefits

  • Your guide to learning efficient machine learning processes from scratch
  • Explore expert techniques and hacks for a variety of machine learning concepts
  • Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems

Description

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.

Who is this book for?

If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

What you will learn

  • Get a quick rundown of model selection, statistical modeling, and cross-validation
  • Choose the best machine learning algorithm to solve your problem
  • Explore kernel learning, neural networks, and time-series analysis
  • Train deep learning models and optimize them for maximum performance
  • Briefly cover Bayesian techniques and sentiment analysis in your NLP solution
  • Implement probabilistic graphical models and causal inferences
  • Measure and optimize the performance of your machine learning models
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Table of Contents

12 Chapters
Quantifying Learning Algorithms Chevron down icon Chevron up icon
Quantifying Learning Algorithms
Statistical models
Learning curve
Curve fitting
Statistical modeling – the two cultures of Leo Breiman
Training data development data – test data
Bias-variance trade off
Regularization
Cross-validation and model selection
Model selection using cross-validation
0.632 rule in bootstrapping
Model evaluation
Receiver operating characteristic curve
H-measure
Dimensionality reduction
Summary
Evaluating Kernel Learning Chevron down icon Chevron up icon
Evaluating Kernel Learning
Introduction to vectors
Linear separability
Hyperplanes 
SVM
Kernel trick
Kernel types
SVM example and parameter optimization through grid search
Summary
Performance in Ensemble Learning Chevron down icon Chevron up icon
Performance in Ensemble Learning
What is ensemble learning?
Bagging
Decision tree
Random forest algorithm
Boosting
Summary
Training Neural Networks Chevron down icon Chevron up icon
Training Neural Networks
Neural networks
Network initialization
Overfitting
Prevention of overfitting in NNs
Vanishing gradient 
Recurrent neural networks
Summary
Time Series Analysis Chevron down icon Chevron up icon
Time Series Analysis
Introduction to time series analysis
White noise
Random walk
Autoregression
Autocorrelation
Stationarity
AR model
Moving average model
Autoregressive integrated moving average
Optimization of parameters
Anomaly detection
Summary
Natural Language Processing Chevron down icon Chevron up icon
Natural Language Processing
Text corpus
TF-IDF
Sentiment analysis
Topic modeling 
The Bayes theorem
Summary
Temporal and Sequential Pattern Discovery Chevron down icon Chevron up icon
Temporal and Sequential Pattern Discovery
Association rules
Apriori algorithm
Frequent pattern growth
Summary
Probabilistic Graphical Models Chevron down icon Chevron up icon
Probabilistic Graphical Models
Key concepts
Bayes rule
Bayes network
Summary
Selected Topics in Deep Learning Chevron down icon Chevron up icon
Selected Topics in Deep Learning
Deep neural networks
Backward propagation
Forward propagation equation
Backward propagation equation
Parameters and hyperparameters
Bias initialization
Generative adversarial networks
Hinton's Capsule network
Summary
Causal Inference Chevron down icon Chevron up icon
Causal Inference
Granger causality
F-test
Graphical causal models
Summary
Advanced Methods Chevron down icon Chevron up icon
Advanced Methods
Introduction
Kernel PCA
Independent component analysis
Compressed sensing
Self-organizing maps
Bayesian multiple imputation
Summary
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About the author

Profile icon Kumar
Kumar
Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.
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Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela