Bestvars feature_selection methods
Project description
Best Variables for classification and regression models
The objective of this packege is to simplify the usage of methods to make feature selection.
The Package bestvars_pk is a Python module for machine learning built based on top of sklearn feature_selection and is distributed under the license.
The project was started in 2020 by Gutelvam as a Udacity Nanodegree of project.
Installation
Dependencies
bestvars_pk requires:
-Python (>= 3.6)
-NumPy (>= 1.13.3)
-SciPy (>= 0.19.1)
-joblib (>= 0.11)
-threadpoolctl (>= 2.0.0)
-scikit-learn (>=0.23.1)
-matplotlib(>=3.2.2)
-seaborn(>=0.10.1)
-pandas(>=1.0.3)
User installation
If you already have a working installation of scikit-learn, the easiest way to install is using pip
!pip install best-vars-pk
How to use
1. Instantiate an object 'Selection'
from bestvarspk.Variables_selection import Selection
obj = Selecton(df, target)
where:
df is a dataframe
target is a string of target column name
2. Use methods available.
obj.corr_features()
obj.importance_features()
obj.rfe_features()
obs: Anytime you can check for help(?) to check docstrings.
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