There are many different linear regression models built-in in Scikit-learn, Ordinary Least Squares (OLS) and Least Absolute Shrinkage and Selection Operator (LASSO) to name two. The difference between these two can be approximated by different loss functions, which is the function that is worked on by the machine learning algorithm. In LASSO, there is an added penalty going away from the fitted function, whereas OLS is simply the least square equation. However, the routine is still different from the OLS that we covered earlier; the underlying algorithm to reach the answer is a machine learning algorithm. One such common algorithm is gradient decent. Here, we shall take the climate data from the previous chapter and fit a linear function to it with two methods, then we will compare the results from the OLS model with that of PyMC's Bayesian inference ( Chapter 6 , Bayesian Methods) and statsmodels' OLS ( Chapter 4 , Regression).
Mastering Python Data Analysis
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Mastering Python Data Analysis
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Overview of this book
Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!
Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.
You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
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Mastering Python Data Analysis
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Table of Contents (15 chapters)
Mastering Python Data Analysis
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Tools of the Trade
Exploring Data
Learning About Models
Regression
Clustering
Bayesian Methods
Supervised and Unsupervised Learning
Time Series Analysis
More on Jupyter Notebook and matplotlib Styles
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