modelselect
A package helps easily create an optimal linear regression model by removing the insignificant and multicollinearity predictor variables, which can help you reduce the interactive process and tedious work to run the model, estimate it, evaluate it, reestimate and reevaluate it, etc.
Developed by Shouke Wei from Deepsim Academy, Deepsim Intelligence Technology Inc. (c) 2022
Install the package
pip install modelselect
import the package
from modelselect import LRSelector
then use the LRSelector() directly. Or
import modelselect as ms
then use ms.LRSelector()
Document
An example: https://github.com/shoukewei/modelselect/blob/main/docs/example.ipynb
1 maintainer
Release files for modelselect 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| modelselect-0.0.1.tar.gz | 2.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| modelselect-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.7 kB
Release files / modelselect-0.0.1.tar.gz
| Download URL | modelselect-0.0.1.tar.gz |
|---|---|
| Size | 2.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / modelselect-0.0.1-py3-none-any.whl
| Download URL | modelselect-0.0.1-py3-none-any.whl |
|---|---|
| Size | 2.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.13
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