Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Stacking and majority voting for multiple models


It is generally believed that two people know more than one person alone. A democracy should work better than a dictatorship. In machine learning, we don't have humans making decisions, but algorithms. When we have multiple classifiers or regressors working together, we speak of ensemble learning.

There are many ensemble learning schemes. The simplest setup does majority voting for classification and averaging for regression. In scikit-learn 0.17, you can use the VotingClassifier class to do majority voting. This classifier lets you emphasize or suppress classifiers with weights.

Stacking takes the outputs of machine learning estimators and then uses those as inputs for another algorithm. You can, of course, feed the output of the higher-level algorithm to another predictor. It is possible to use any arbitrary topology, but for practical reasons, you should try a simple setup first.

How to do it...

  1. The imports are as follows:

    import dautil as...