Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

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.

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Table of Contents (18 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Quantifying Learning Algorithms
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
2
Evaluating Kernel Learning
Evaluating Kernel Learning
Introduction to vectors
Linear separability
Hyperplanes 
SVM
Kernel trick
Kernel types
SVM example and parameter optimization through grid search
Summary
3
Performance in Ensemble Learning
Performance in Ensemble Learning
What is ensemble learning?
Bagging
Decision tree
Random forest algorithm
Boosting
Summary
4
Training Neural Networks
Training Neural Networks
Neural networks
Network initialization
Overfitting
Prevention of overfitting in NNs
Vanishing gradient 
Recurrent neural networks
Summary
5
Time Series Analysis
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
6
Natural Language Processing
Natural Language Processing
Text corpus
TF-IDF
Sentiment analysis
Topic modeling 
The Bayes theorem
Summary
7
Temporal and Sequential Pattern Discovery
Temporal and Sequential Pattern Discovery
Association rules
Apriori algorithm
Frequent pattern growth
Summary
8
Probabilistic Graphical Models
Probabilistic Graphical Models
Key concepts
Bayes rule
Bayes network
Summary
9
Selected Topics in Deep Learning
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
10
Causal Inference
Causal Inference
Granger causality
F-test
Graphical causal models
Summary
11
Advanced Methods
Advanced Methods
Introduction
Kernel PCA
Independent component analysis
Compressed sensing
Self-organizing maps
Bayesian multiple imputation
Summary
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Index
Index

Bayesian multiple imputation

Bayesian multiple imputation has got the spirit of the Bayesian framework. It is required to specify a parametric model for the complete data and a prior distribution over unknown model parameters, θ. Subsequently, m independent trials are drawn from the missing data, as given by the observed data using Bayes' Theorem. Markov Chain Monte Carlo can be used to simulate the entire joint posterior distribution of the missing data. BMI follows a normal distribution while generating imputations for the missing values.

Let's say that the data is as follows:

Y = (Yobs, Ymiss),

Here, Yobs is the observed Y and Ymiss is the missing Y.

 

 If P(Y|θ) is the parametric model, the parameter θ is the mean and the covariance matrix that parameterizes a normal distribution. If this is the case, let P(θ) be the prior:

Let's make use of the Amelia package in R and execute this:

library(foreign)
dataset = read.spss("World95.sav", to.data.frame=TRUE)

library(Amelia)

myvars <- names...
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