| Name | Pratik-model JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | None |
| Summary | This package directly gives you output performance on 12 different algorithms |
| upload_time | 2024-11-16 17:25:43 |
| maintainer | None |
| docs_url | None |
| author | pratik |
| requires_python | None |
| license | MIT |
| keywords |
pratik_model
|
| VCS |
 |
| bugtrack_url |
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No requirements were recorded.
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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!!.
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"description": "Pratik_model\r\n- The best thing about this package is that you do not have to train and predict every classification or regression algorithm to check performance.\r\n- This package directly gives you output performance on 13 different algorithms.\r\n\r\nHow to use it - \r\nFor Classification\r\nx= Independent variables\r\ny= Dependent variables\r\n\r\n* From Pratik_model import smart_classifier\r\n* model = smart_classifier(x,y)\r\n* model.accuracy_score()\r\n* model.classification_report()\r\n* model.confusion_matrix()\r\n* model.cross_validation()\r\n* model.mean_absolute_error()\r\n* model.precision_score()\r\n* model.recall_score()\r\n* model.mean_absolute_error()\r\n* model.mean_absolute_error()\r\n* model.mean_squared_error()\r\n* model.cross_validation()\r\n\r\nFor Regression -\r\n\r\n* From Pratik_model import smart_regressor\r\n* model=smart_regressor(x,y)\r\n* model.r2_score()\r\n* model.mean_absolute_error()\r\n* model.mean_absolute_error()\r\n* model.mean_squared_error()\r\n* model.cross_validation()\r\n* model.overfitting()\r\n\r\nCheck Pratik_Model_Package.ipynb file on Github for practical code.\r\n\r\nPratik_model for Classification: \r\nIt will check the performance on this Classification models:\r\n- Passive Aggressive Classifier\r\n- Decision Tree Classifier\r\n- Random Forest Classifier\r\n- Extra Trees Classifier\r\n- Logistic Regression\r\n- Ridge Classifier\r\n- K Neighbors Classifier\r\n- Support Vector Classification\r\n- Naive Bayes Classifier\r\n- LGBM Classifier\r\n- CatBoost Classifier\r\n- XGB Classifier\r\n\r\nAnd for classification problems Pratik_model can give the output of:\r\n- Accuracy Score.\r\n- Classification Report\r\n- Confusion Matrix\r\n- Cross validation (Cross validation score)\r\n- Mean Absolute Error\r\n- Mean Squared Error\r\n- Overfitting (will give accuracy of training and testing data.)\r\n- Precision Score\r\n- Recall Score\r\n\r\nPratik_model for Regression: \r\nSimilarly, It will check performance on this Regression model:\r\n- Passive Aggressive Regressor\r\n- Gradient Boosting Regressor\r\n- Decision Tree Regressor\r\n- Random Forest Regressor\r\n- Extra Trees Regressor\r\n- Lasso Regression\r\n- K Neighbors Regressor\r\n- Linear Regression\r\n- Support Vector Regression\r\n- LGBM Regressor\r\n- CatBoost Regressor\r\n- XGB Regressor\r\n\r\nAnd for Regression problem Pratik_model\r\ncan give an output of:\r\n- R2 Score.\r\n- Cross validation (Cross validation score)\r\n- Mean Absolute Error\r\n- Mean Squared Error\r\n- Overfitting (will give accuracy of training and testing data.)\r\n\r\n\r\nFirst Release\r\n0.0.7 (29/3/2022)\r\n\r\nThank You!!.\r\n",
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