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Learning Predictive Analytics with Python

You're reading from   Learning Predictive Analytics with Python Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783983261
Length 354 pages
Edition 1st Edition
Languages
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Authors (2):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Gary Dougan Gary Dougan
Author Profile Icon Gary Dougan
Gary Dougan
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Predictive Modelling FREE CHAPTER 2. Data Cleaning 3. Data Wrangling 4. Statistical Concepts for Predictive Modelling 5. Linear Regression with Python 6. Logistic Regression with Python 7. Clustering with Python 8. Trees and Random Forests with Python 9. Best Practices for Predictive Modelling A. A List of Links
Index

Hypothesis testing


The concept we just discussed in the preceding section is used for a very important technique in statistics, called hypothesis testing. In hypothesis testing, we assume a hypothesis (generally related to the value of the estimator) called null hypothesis and try to see whether it holds true or not by applying the rules of a normal distribution. We have another hypothesis called alternate hypothesis.

Null versus alternate hypothesis

There is a catch in deciding what will be the null hypothesis and what will be the alternate hypothesis. The null hypothesis is the initial premise or something that we assume to be true as yet. The alternate hypothesis is something we aren't sure about and are proposing as an alternate premise (almost often contradictory to the null hypothesis) which might or might not be true.

So, when someone is doing a quantitative research to calibrate the value of an estimator, the known value of the parameter is taken as the null hypothesis while the new...

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