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

Bagging to improve results


Bootstrap aggregating or bagging is an algorithm introduced by Leo Breiman in 1994, which applies bootstrapping to machine learning problems. Bagging was also mentioned in the Learning with random forests recipe.

The algorithm aims to reduce the chance of overfitting with the following steps:

  1. We generate new training sets from input training data by sampling with replacement.

  2. Fit models to each generated training set.

  3. Combine the results of the models by averaging or majority voting.

The scikit-learn BaggingClassifier class allows us to bootstrap training examples, and we can also bootstrap features as in the random forests algorithm. When we perform a grid search, we refer to hyperparameters of the base estimator with the prefix base_estimator__. We will use a decision tree as the base estimator so that we can reuse some of the hyperparameter configuration from the Learning with random forests recipe.

How to do it...

The code is in the bagging.ipynb file in this book...