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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Learn advanced techniques to improve the performance and quality of your predictive models
Key Features
- Use ensemble methods to improve the performance of predictive analytics models
- Implement feature selection, dimensionality reduction, and cross-validation techniques
- Develop neural network models and master the basics of deep learning
Book Description
Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.
By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
What you will learn
- Use ensemble algorithms to obtain accurate predictions
- Apply dimensionality reduction techniques to combine features and build better models
- Choose the optimal hyperparameters using cross-validation
- Implement different techniques to solve current challenges in the predictive analytics domain
- Understand various elements of deep neural network (DNN) models
- Implement neural networks to solve both classification and regression problems
Who this book is for
Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
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Seitenzahl: 126
Veröffentlichungsjahr: 2018
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Mastering Predictive Analytics with scikit-learn and TensorFlow
Copyright © 2018 Packt Publishing
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First published: September 2018
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ISBN 978-1-78961-774-0
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Contributor
About the author
Alan Fontaine is a data scientist with more than 12 years of experience in analytical roles. He has been a consultant for many projects in fields such as: business, education, medicine, mass media, among others. He is a big Python fan and has been using it routinely for five years for analyzing data, building models, producing reports, making predictions, and building interactive applications that transform data into intelligence.
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Table of Contents
Title Page
Copyright and Credits
Mastering Predictive Analytics with scikit-learn and TensorFlow
Packt Upsell
Why subscribe?
Packt.com
Contributor
About the author
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Ensemble Methods for Regression and Classification
Ensemble methods and their working
Bootstrap sampling
Bagging
Random forests
Boosting
Ensemble methods for regression
The diamond dataset
Training different regression models
KNN model
Bagging model
Random forests model
Boosting model
Using ensemble methods for classification
Predicting a credit card dataset 
Training different regression models
Logistic regression model
Bagging model
Random forest model
Boosting model
Summary
Cross-validation and Parameter Tuning
Holdout cross-validation
K-fold cross-validation
Implementing k-fold cross-validation
Comparing models with k-fold cross-validation
Introduction to hyperparameter tuning
Exhaustive grid search
Hyperparameter tuning in scikit-learn
Comparing tuned and untuned models
Summary
Working with Features
Feature selection methods 
Removing dummy features with low variance
Identifying important features statistically
Recursive feature elimination
Dimensionality reduction and PCA
Feature engineering
Creating new features
Improving models with feature engineering
Training your model
Reducible and irreducible error
Summary
Introduction to Artificial Neural Networks and TensorFlow
Introduction to ANNs
Perceptrons
Multilayer perceptron
Elements of a deep neural network model
Deep learning
Elements of an MLP model
Introduction to TensorFlow
TensorFlow installation
Core concepts in TensorFlow
Tensors
Computational graph
Summary
Predictive Analytics with TensorFlow and Deep Neural Networks
Predictions with TensorFlow
Introduction to the MNIST dataset
Building classification models using MNIST dataset
Elements of the DNN model
Building the DNN
Reading the data
Defining the architecture
Placeholders for inputs and labels
Building the neural network
The loss function
Defining optimizer and training operations
Training strategy and valuation of accuracy of the classification
Running the computational graph
Regression with Deep Neural Networks (DNN)
Elements of the DNN model
Building the DNN
Reading the data
Objects for modeling
Training strategy
Input pipeline for the DNN
Defining the architecture
Placeholders for input values and labels
Building the DNN
The loss function
Defining optimizer and training operations
Running the computational graph
Classification with DNNs
Exponential linear unit activation function
Classification with DNNs
Elements of the DNN model
Building the DNN
Reading the data
Producing the objects for modeling
Training strategy
Input pipeline for DNN
Defining the architecture
Placeholders for inputs and labels
Building the neural network
The loss function
Evaluation nodes
Optimizer and the training operation
Run the computational graph
Evaluating the model with a set threshold
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Python is a programming language that provides various features in the field of data science. In this book, we will be touching upon two Python libraries, scikit-learn and TensorFlow. We will learn about the various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with studying ensemble methods and their features. We will look at how scikit-learn provides the right tools to choose hyperparameters for models. From there, we will get down to the nitty-gritty of predictive analytics and explore its various features and characteristics. We will be introduced to artificial neural networks, TensorFlow, and the core concepts used to build neural networks.In the final section, we will consider factors such as computational power, improved methods, and software enhancements for efficient predictive analytics. You will become well versed in using DNNs to solve common challenges.
Who this book is for
This book is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to go from basic predictive models to building advanced models and producing better predictions. Knowledge of Python and familiarity with predictive analytics concepts are assumed.
What this book covers
Chapter 1, Ensemble Methods for Regression and Classification, covers the application of ensemble methods or algorithms to produce accurate predictions of models. We will go through the application of ensemble methods for regression and classification problems.
Chapter 2, Cross-validation and Parameter Tuning, explores various techniques to combine and build better models. We will learn different methods of cross-validation, including holdout cross-validation and k-fold cross-validation. We will also discuss what hyperparameter tuning is.
Chapter 3, Working with Features, explores feature selection methods, dimensionality reduction, PCA, and feature engineering. We will also study methods to improve models with feature engineering.
Chapter 4, Introduction to Artificial Neural Networks and TensorFlow, is an introduction to ANNs and TensorFlow. We will explore the various elements in the network and their functions. We will also learn the basic concepts of TensorFlow in it.
Chapter 5, Predictive Analytics with TensorFlow and Deep Neural Networks, explores predictive analytics with the help of TensorFlow and deep learning. We will study the MNIST dataset and classification of models using this dataset. We will learn about DNNs, their functions, and the application of DNNs to the MNIST dataset.
To get the most out of this book
This book presents some of the most advanced predictive analytics tools, models, and techniques. The main goal is to show the viewer how to improve the performance of predictive models, firstly, by showing how to build more complex models, and secondly by showing how to use related techniques that dramatically improve the quality of predictive models.
Download the example code files
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mastering-Predictive-Analytics-with-scikit-learn-and-TensorFlow. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789617740_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The following screenshot shows the lines of code used for importing the train_test_split function and the RobustScaler method."
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Ensemble Methods for Regression and Classification
Advanced analytical tools are widely used by business enterprises in order to solve problems using data. The goal of analytical tools is to analyze data and extract relevant information that can be used to solve problems or increase performance of some aspect of the business. It also involves various machine learning algorithms with which we can create predictive models for better results.
In this chapter, we are going to explore a simple idea that can drastically improve the performance of basic predictive models.
We are going to cover the following topics in this chapter:
Ensemble methods and their working
Ensemble methods for regression
Ensemble methods for classification
Ensemble methods and their working
Ensemble methods are based on a very simple idea: instead of using a single model to make a prediction, we use many models and then use some method to aggregate the predictions. Having different models is like having different points of view, and it has been demonstrated that by aggregating models that offer a different point of view; predictions can be more accurate. These methods further improve generalization over a single model because they reduce the risk of selecting a poorly performing classifier:
In the preceding diagram, we can see that each object belongs to one of three classes: triangles, circles, and squares. In this simplified example, we have two features to separate or classify the objects into the different classes. As you can see here, we can use three different classifiers and all the three classifiers represent different approaches and have different kinds of decision boundaries.
