Statistical-Learning | Statistical Learning Methods ( Li Hang

by librauee Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Statistical-Learning Summary

Statistical-Learning is a Python library. Statistical-Learning has no bugs, it has no vulnerabilities and it has low support. However Statistical-Learning build file is not available. You can download it from GitHub.

✒Second Edition of Statistical Learning Methods (Li Hang) Study notes, code implementation, after-school exercises

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Quality

Security

License

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Support

Statistical-Learning has a low active ecosystem.

It has 321 star(s) with 99 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

Statistical-Learning has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Statistical-Learning is current.

Quality

Statistical-Learning has 0 bugs and 99 code smells.

Security

Statistical-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Statistical-Learning code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

Statistical-Learning does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Statistical-Learning releases are not available. You will need to build from source code and install.

Statistical-Learning has no build file. You will be need to create the build yourself to build the component from source.

Statistical-Learning saves you 475 person hours of effort in developing the same functionality from scratch.

It has 1120 lines of code, 106 functions and 17 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Statistical-Learning and discovered the below as its top functions. This is intended to give you an instant insight into Statistical-Learning implemented functionality, and help decide if they suit your requirements.

Compute the classifier
Classify features
Compute the test accuracy
Predict a feature
Fit the model
Calculate the kernel function
A helper function for comparing alpha
Calculate the G function
Find the nearest node in a tree
Return the distance between two points
Computes the score of the test
Compute the accuracy of the test
Calculate the sum of the matrix
Compute the prediction score
Fit GaussianNB
Preorder nodes
Create group and labels
Checks whether the kt is a kt
Check if all values are equal
Computes the info gain for the given dataset
Train the model
Create the data structure
Classify person classification
Calculates the test set test vectors
Classify input data using classification
Creates samples and labels

Get all kandi verified functions for this library.

Statistical-Learning Key Features

No Key Features are available at this moment for Statistical-Learning.

Statistical-Learning Examples and Code Snippets

No Code Snippets are available at this moment for Statistical-Learning.

Community Discussions

Trending Discussions on Statistical-Learning

Shading background based on groups above/below a line

QUESTION

Shading background based on groups above/below a line

Asked 2017-Aug-20 at 11:03

Suppose I have a scatterplot with some kind of line (least squares regression line, knn regression line, etc.) through it, like this. I want to shade the upper region of the plot reddish, and the lower region of the plot blueish, to give an indication of how my line is doing as a classifier for the points. Similar to my mimic example with this effect is this plot from Elements of Statistical Learning (Hastie et al), (Chapter 2, page 13).

How can I achieve this effect with Matplotlib?

I know how to set rectangular regions of a plot to be different colors with axhspan and axvspan (see this answer), but have been struggling to set different plot colors based on regions above and below a line.

Code to replicate my current mock plot ...

ANSWER

Answered 2017-Aug-20 at 11:03

First I would recommend sorting the x values, such that the line looks smooth.

Source https://stackoverflow.com/questions/45778290

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Statistical-Learning

You can download it from GitHub.
You can use Statistical-Learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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