semvec/pypi-packages huggingface dataset:
Smart-model 0.0.1
This package directly gives you output performance on different models
License: MIT
Keywords: Smart_model

- 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 donâ€™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 Smart_model
 there are two classes present which is smart_classifier(For Classification problems) and smart_regressor (for Regression problems).
# Smart_model Package
- In Smart_model
 Package.IPYNB you can see how to use it.


# Smart_model for Classification: 
Smart_model
 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 Smart_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


# Smart_model
 for Regression: 
Similarly, Smart_model
 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 Smart_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!!.


Change Log
==========

0.0.1 (29/3/2022)
-------------------
- First Release
License-File: LICENSE.txt