distfit | distfit is a python library for probability density fitting | Analytics library

by erdogant Jupyter Notebook Version: 1.6.10 License: Non-SPDX

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

kandi X-RAY | distfit Summary

distfit is a Jupyter Notebook library typically used in Analytics, Numpy applications. distfit has no bugs, it has no vulnerabilities and it has low support. However distfit has a Non-SPDX License. You can download it from GitHub.

Python package for probability density function fitting of univariate distributions of non-censored data

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Support

distfit has a low active ecosystem.

It has 265 star(s) with 18 fork(s). There are 7 watchers for this library.

It had no major release in the last 12 months.

There are 6 open issues and 24 have been closed. On average issues are closed in 46 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of distfit is 1.6.10

Quality

distfit has 0 bugs and 0 code smells.

Security

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

distfit code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

distfit 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

distfit releases are available to install and integrate.

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

It has 4789 lines of code, 31 functions and 32 files.

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

Top functions reviewed by kandi - BETA

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

Apply smoothing to the data
Compute the fit score
Compute confidence interval
Compute the model for the given distribution
Plot the distribution
Plot empirical quantile
Plot the parameter distribution
Plot binom
Predict the distribution
Perform a multtest fitting on the distribution
Predict percentile
Compute the probability of the fitted distribution
Fit the model to data
Computes the best fitting of the given data
Clean the model results
Creates a dictionary
Plot a summary of the distribution
Save model to file
Load a model from a file
Generate n samples

Get all kandi verified functions for this library.

distfit Key Features

No Key Features are available at this moment for distfit.

distfit Examples and Code Snippets

No Code Snippets are available at this moment for distfit.

Community Discussions

Trending Discussions on distfit

Should the data preparation phase for ML include both: fitting data to right distribution followed by Scaling?

QUESTION

Should the data preparation phase for ML include both: fitting data to right distribution followed by Scaling?

Asked 2021-Sep-23 at 05:09

I have used distfit library to find the best distribution that will fit my data to avoid skewness. Let us say, I have transformed my data into Normal distribution using the boxcox method.

After this, shall I scale my data, for example, using Robust Scaler that handles outliers very well.

I am confused that I should be following both the steps or just one.

Not sure, if I am heading in the right direction in the data prep phase. please share your thoughts on this. Thanks!

...

ANSWER

Answered 2021-Sep-23 at 05:09

You might or might have to do scaling after Normalization.

Answer depends on what are we doing to this data. e.g. Are we planing to fit some model? or anything else?

One concrete example is:

If want to train our model for Neural Networks, then let see:

For faster convergence of training: We should have mean= 0 and sigma=1 (Normalization needed)
For effective regularization, you mush have all the data features at similar scale. (Scaling needed)

On contrast, if you want to fit say Decision Tree, then neither of these things are needed.

So, it all boils down to what we have to do after processing the data.

Source https://stackoverflow.com/questions/69292689

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

Vulnerabilities

No vulnerabilities reported

Install distfit

You can download it from GitHub.

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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