Elastic net regularization is a method that reduces the danger of overfitting in the context of regression (see http://en.wikipedia.org/wiki/Elastic_net_regularization). The elastic net regularization linearly combines the least absolute shrinkage and selection operator (LASSO) and ridge methods. LASSO limits the so-called L1 norm, or Manhattan distance. This norm measures the sum of the difference between the absolute coordinates of a pair of points. The ridge method uses a penalty, which is the L1 norm squared. For regression problems, the goodness of fit is often determined using the coefficient of determination , also called R squared (see http://en.wikipedia.org/wiki/Coefficient_of_determination). Unfortunately, there are several definitions of R squared. Also, the name is a bit misleading, since negative values are possible. A perfect fit would have a coefficient of determination of 1. Since the definitions allow for a wide range of acceptable values, we...
Python Data Analysis - Second Edition
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Python Data Analysis - Second Edition
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Overview of this book
Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.
With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.
The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
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Current Title:
Python Data Analysis - Second Edition
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Python Libraries
NumPy Arrays
The Pandas Primer
Statistics and Linear Algebra
Retrieving, Processing, and Storing Data
Data Visualization
Signal Processing and Time Series
Working with Databases
Analyzing Textual Data and Social Media
Predictive Analytics and Machine Learning
Environments Outside the Python Ecosystem and Cloud Computing
Performance Tuning, Profiling, and Concurrency
Key Concepts
Useful Functions
Online Resources
Customer Reviews