KTBoost | Python package which implements several boosting algorithms | Machine Learning library

by fabsig Python Version: 0.2.2 License: Non-SPDX

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

kandi X-RAY | KTBoost Summary

KTBoost is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Example Codes applications. KTBoost has no bugs, it has no vulnerabilities, it has build file available and it has low support. However KTBoost has a Non-SPDX License. You can install using 'pip install KTBoost' or download it from GitHub, PyPI.

Concerning base learners, KTBoost includes:.

Support

Quality

Security

License

Reuse

Support

KTBoost has a low active ecosystem.

It has 31 star(s) with 12 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 6 have been closed. On average issues are closed in 77 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of KTBoost is 0.2.2

Quality

KTBoost has 0 bugs and 0 code smells.

Security

KTBoost has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

KTBoost code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

KTBoost has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

KTBoost releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

It has 3517 lines of code, 213 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Fit intercept function
R Ridge ridge regression
Solve the Cholesky kernel
Solve the Cholesky decomposition
Predict the log probabilities of the model
Predict the class of the model
Predict class probabilities for the model
Fit the model
Fit the regression model
Fit Grid SearchCV
Compute partial dependencies for a given model
Generate grid axes based on quantiles
Evaluate the model
Validates X
Update the regions of the tree
Reduce the y_pred to the given value
Predict classes for the decision function
Generate the predicted classes for each category
Plot 2D function
Predict log probabilities for X
Returns the staged decision function
Returns a series of predicted data
Update the regions of the terminal
Predict the class of X
Return a generator of predicted probabilities
Function that calculates the fct of a nonlinear function

Get all kandi verified functions for this library.

KTBoost Key Features

No Key Features are available at this moment for KTBoost.

KTBoost Examples and Code Snippets

No Code Snippets are available at this moment for KTBoost.

Community Discussions

Trending Discussions on KTBoost

What causes the singular matrix error in kernel ridge regression and how to fix it?

QUESTION

What causes the singular matrix error in kernel ridge regression and how to fix it?

Asked 2020-Oct-20 at 07:39

When building a KTBoost model, I got the following error message:

...

ANSWER

Answered 2020-Oct-20 at 07:39

A singular matrix is very likely due to features that are very close to each other (or duplicates). I assume that you have tried adding noise to the features since adding noise to the labels does not help with this. In any case, it is also possible to have duplicate features but one then needs to add some regularization using the parameter 'alphaReg'. This values is added to the diagonal of the kernel matrix and thus helps to avoid singular matrices.

What value have you set for the regularization parameter 'alphaReg'? Until version 0.1.18, the default values was wrongly set to 0 instead of 1, which is what the documentation says and also what scikit-learn is using. I have corrected this now. Can you please check whether the error still occurs when using KTBoost version >= 0.1.19?

If this does not solve the issue, can you please provide a minimal working example with data and code to reproduce the error? Otherwise, it is difficult to tell what is happening.

In the future, you might also open an issue on https://github.com/fabsig/KTBoost. It will be answered faster there.

Source https://stackoverflow.com/questions/62538759

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

Vulnerabilities

No vulnerabilities reported

Install KTBoost

It can be installed using. and then loaded using.

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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