A simple Python Package for Model Evalutaion
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About The Project
A very elegant and simple library to evaluate models.
This module will evaulate the Classification problems and Regression problems with 13 and 7 algorithms respectively.
The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, XgBoost,AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.
The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression, PLSRegression.
We also have implmented the Adjusted R Squared method as the Regression Metric Evaluation.
In Classification , Highest Accuracy is Highlighted in Yellow colour.
In Regression Model , Least Error is Highlighted in Yellow colour.
Installation
- Clone the repo
git clone https://github.com/Anand-gokul/pyevals.git
- Install using pip or pip3
pip3 install pyevals==1.0
(or)
pip install pyevals==1.0
Usage
import pyevals
# For Classification
Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
Object.evaluate()
# For Regression
Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
Object.evaluate()
Future Work
In this version we are only providing the reports, but soon we will be adding plots too.We will also try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon.
Contact
Sai Gokul Krishna Reddy Talla - @Krish - gokulkrishna.talla@gmail.com
Ananda Datta Sai Phanindra Tangirala - @Anand - tangiralaphanindra@gmail.com
Anirudh Palaparthi - @anirudh8889 - aniruddhapnbb@gmail.com
Project Link: https://github.com/Anand-Gokul/pyevals
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