Book Image

Training Systems using Python Statistical Modeling

By : Curtis Miller
Book Image

Training Systems using Python Statistical Modeling

By: Curtis Miller

Overview of this book

Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them. By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
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1
Classical Statistical Analysis
Classical Statistical Analysis
Technical requirements
Computing descriptive statistics
Classical inference for proportions
Classical inference for means
Diving into Bayesian analysis
Bayesian analysis for proportions
Bayesian analysis for means
Finding correlations
Summary
2
Introduction to Supervised Learning
Introduction to Supervised Learning
Principles of machine learning
Training models
Evaluating models
Summary
3
Binary Prediction Models
Binary Prediction Models
K-nearest neighbors classifier
Decision trees
Random forests
Naive Bayes classifier
Support vector machines
Logistic regression
Extending beyond binary classifiers
Summary
4
Regression Analysis and How to Use It
Regression Analysis and How to Use It
Linear models
Evaluating linear models
Bayesian linear models
Ridge regression
LASSO regression
Spline interpolation
Summary
5
Neural Networks
Neural Networks
An introduction to perceptrons
Neural networks
MLPs for classification
MLP for regression
Summary
6
Clustering Techniques
Clustering Techniques
Introduction to clustering
Exploring the k-means algorithm
Evaluating clusters
Hierarchical clustering
Spectral clustering
Summary
7
Dimensionality Reduction
Dimensionality Reduction
Introducing dimensionality reduction
Principal component analysis
Singular value decomposition
Low-dimensional representation
Summary
8
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Evaluating linear models

In this section, we will examine a number of metrics that we can use to evaluate the performance of a linear model other than using the MSE and cross-validation. We will look at some of the statistical tests and metrics that are used to evaluate how well a linear model performs, and to help decide between different linear model forms.

There are two statistical tests to be aware of for linear models, as follows:

  • First, is the test for whether one particular coefficient in the model is 0 or not. Failing to reject the null hypothesis indicates that the feature does not seem to contribute much to predictions. The following formulas show these hypotheses:
  • Second, is an overall test, that is, the F-test. This tests whether any features have coefficients that are nonzero. Rejecting the null hypothesis suggests that your model has some predictive ability....
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