Pratik-model 0.0.4

This package directly gives you output performance on different models

This package can be used in machine learning (Data Science) to check the performance of models.

The best thing about this package is that you don’t have to train and predict every classification or regression algorithm to check performance. This package directly gives you output performance on different models.

In Lazy_pratik_model

 there are two classes present which is smart_classifier(For Classification problems) and smart_regressor (for Regression problems).

Lazy_pratik_model for Classification: 



 will check the performance on this Classification models:

Passive Aggressive Classifier

Decision Tree Classifier

Random Forest Classifier

Extra Trees Classifier

Logistic Regression

Ridge Classifier

K Neighbors Classifier

Support Vector Classification

Naive Bayes Classifier

LGBM Classifier

CatBoost Classifier

XGB Classifier



And for classification problems Lazy_pratik_model can give the output of:

Accuracy Score.

Classification Report

Confusion Matrix

Cross validation (Cross validation score)

Mean Absolute Error

Mean Squared Error

Overfitting (will give accuracy of training and testing data.)

Precision Score

Recall Score



Lazy_pratik_model for Regression: 



Similarly, will check performance on this Regression model:

Passive Aggressive Regressor

Gradient Boosting Regressor

Decision Tree Regressor

Random Forest Regressor

Extra Trees Regressor

Lasso Regression

K Neighbors Regressor

Linear Regression

Support Vector Regression

LGBM Regressor

CatBoost Regressor

XGB Regressor



And for Regression problem Lazy_pratik_model

 can give an output of:

R2 Score.

Cross validation (Cross validation score)

Mean Absolute Error

Mean Squared Error

Overfitting (will give accuracy of training and testing data.)



Thank You!!.



License-File: LICENSE.txt