support-vector-machine | An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset | Machine Learning library

by AFAgarap Python Version: v0.1.5-alpha License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | support-vector-machine Summary

support-vector-machine is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. support-vector-machine has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, GitLab.

An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.

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support-vector-machine has a low active ecosystem.

It has 17 star(s) with 7 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 1 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of support-vector-machine is v0.1.5-alpha

Quality

support-vector-machine has 0 bugs and 0 code smells.

Security

support-vector-machine has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

support-vector-machine code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

support-vector-machine is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

support-vector-machine releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 217 lines of code, 9 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Train the TensorBoard
Save the predicted and actual labels
Plot the confusion matrix
List all files under path
Parse arguments
Train the model
Performs a single training step

Get all kandi verified functions for this library.

support-vector-machine Key Features

No Key Features are available at this moment for support-vector-machine.

support-vector-machine Examples and Code Snippets

No Code Snippets are available at this moment for support-vector-machine.

Community Discussions

Trending Discussions on support-vector-machine

How do you forecast future values using support vector regression in R

What is pointless data?

Can we plot image data in Altair?

Interpretation of method plt.fill_between()? Discussion

QUESTION

How do you forecast future values using support vector regression in R

Asked 2020-Oct-08 at 12:34

I am trying to forecast for future values of a periodic position dependent on time (x ~ time), univariate forecasting using support vector regression. The model fits well on train data but then trails into a straight line when evaluated on test data. In the code below, I used 50 observations for train (the first half of the red periodic curve, where SVR fits perfectly) and 50 observations for test (the second half of the red curve, where SVR fails to predict).

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ANSWER

Answered 2020-Oct-08 at 12:34

You can use caretForecast package. You can use any ML model which supported by caret including SVM.

to install the package: devtools::install_github("Akai01/caretForecast")

Example code

Source https://stackoverflow.com/questions/64198200

QUESTION

What is pointless data?

Asked 2020-May-07 at 07:36

I'm reading a tutorial about SVM.

He wrote there:

The Support Vector Machine, in general, handles pointless data better than the K Nearest Neighbors algorithm

What does he mean by "pointless data"?

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ANSWER

Answered 2020-May-07 at 07:31

The sentence refers to the sentence before that:

Note that if we comment out the drop id column part, accuracy goes back down into the 60s.

and the KNearestNeighbors tutorial where the change in model performance is investigated if 'useless' data (aka noise), like the indices of the data points, is fed to the model as input.

[...] let's show what happens when we do indeed include truly meaningless and misleading data by commenting out the dropping of the id column

The conclusion here is that SVMs handle meaningless features, noise or 'pointless data' in the input better than KNNs.

Source https://stackoverflow.com/questions/61650954

QUESTION

Can we plot image data in Altair?

Asked 2020-Feb-02 at 01:08

I am trying to plot image data in altair, specifically trying to replicate face recognition example in this link from Jake VDP's book - https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html.

Any one had luck plotting image data in altair?

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ANSWER

Answered 2020-Feb-02 at 01:08

Altair features an image mark that can be used if you want to plot images that are available at a URL; for example:

Source https://stackoverflow.com/questions/60019006

QUESTION

Interpretation of method plt.fill_between()? Discussion

Asked 2020-Jan-20 at 23:26

A question for discussion:

The matplotlib documentation says that the method plt.fill_between is used to "fill the area between two horizontal curves".

What exactly is meant by "horizontal"? Intuitively, I would say "two parallel curves". Like in this example

The curves are not horizontal, but parallel.

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ANSWER

Answered 2020-Jan-19 at 17:07

"Two horizontal curves" is a set of data where you have two arrays y1 and y2 defined on a single support x.
Equally, "two vertical curves" would be a set of data where you have a single y support for two x arrays.

Source https://stackoverflow.com/questions/59812171

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

Vulnerabilities

No vulnerabilities reported

Install support-vector-machine

You can download it from GitHub, GitLab.
You can use support-vector-machine 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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