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Numerical Computing with Python

You're reading from   Numerical Computing with Python Harness the power of Python to analyze and find hidden patterns in the data

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789953633
Length 682 pages
Edition 1st Edition
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Concepts
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Authors (5):
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Pratap Dangeti Pratap Dangeti
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Pratap Dangeti
Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Allen Yu Allen Yu
Author Profile Icon Allen Yu
Allen Yu
Aldrin Yim Aldrin Yim
Author Profile Icon Aldrin Yim
Aldrin Yim
Claire Chung Claire Chung
Author Profile Icon Claire Chung
Claire Chung
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Table of Contents (21) Chapters Close

Title Page
Contributors
About Packt
Preface
1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Tree-Based Machine Learning Models 3. K-Nearest Neighbors and Naive Bayes 4. Unsupervised Learning 5. Reinforcement Learning 6. Hello Plotting World! 7. Visualizing Online Data 8. Visualizing Multivariate Data 9. Adding Interactivity and Animating Plots 10. Selecting Subsets of Data 11. Boolean Indexing 12. Index Alignment 13. Grouping for Aggregation, Filtration, and Transformation 14. Restructuring Data into a Tidy Form 15. Combining Pandas Objects 1. Other Books You May Enjoy Index

Bagging classifier


As we have discussed already, decision trees suffer from high variance, which means if we split the training data into two random parts separately and fit two decision trees for each sample, the rules obtained would be very different. Whereas low variance and high bias models, such as linear or logistic regression, will produce similar results across both samples. Bagging refers to bootstrap aggregation (repeated sampling with replacement and perform aggregation of results to be precise), which is a general purpose methodology to reduce the variance of models. In this case, they are decision trees.

Aggregation reduces the variance, for example, when we have n independent observations x1, x,..., xn each with variance σ2 and the variance of the mean  of the observations is given by σ2/n, which illustrates by averaging a set of observations that it reduces variance. Here, we are reducing variance by taking many samples from training data (also known as bootstrapping),...

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