Pratik-model 0.1.4

This package directly gives you output performance on 12 different algorithms

Pratik_model

• The best thing about this package is that you do not have to train and predict every classification or regression algorithm to check performance.
• This package directly gives you output performance on 13 different algorithms.

How to use it - For Classification x= Independent variables y= Dependent variables

• From Pratik_model import smart_classifier
• model = smart_classifier(x,y)
• model.accuracy_score()
• model.classification_report()
• model.confusion_matrix()
• model.cross_validation()
• model.mean_absolute_error()
• model.precision_score()
• model.recall_score()
• model.mean_absolute_error()
• model.mean_absolute_error()
• model.mean_squared_error()
• model.cross_validation()

For Regression -

• From Pratik_model import smart_regressor
• model=smart_regressor(x,y)
• model.r2_score()
• model.mean_absolute_error()
• model.mean_absolute_error()
• model.mean_squared_error()
• model.cross_validation()
• model.overfitting()

Check Pratik_Model_Package.ipynb file on Github for practical code.

Pratik_model for Classification: It 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 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

Pratik_model for Regression: Similarly, It 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 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.)

First Release 0.0.7 (29/3/2022)

Thank You!!.