.. -*- mode: rst -*-
|Version|_ |PythonVersion|_
.. _Linkedin: https://www.linkedin.com/in/abhishek-kaddipudi-0b5183253
.. _GitHub : https://github.com/Abhishekkaddipudi
.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue
.. _PythonVersion: https://pypi.org/project/modelLab/
.. |Version| image:: https://img.shields.io/badge/Version-V0.1-blue
.. _Version: https://github.com/Abhishekkaddipudi/modelLab
.. |Unit_Test| image:: https://github.com/Abhishekkaddipudi/modelLab/actions/workflows/main.yml/badge.svg
.. _Unit_Test: https://github.com/Abhishekkaddipudi/modelLab
.. _Mail: mailto:abhishekkaddipudi123@gmail.com
**modelLab** is a comprehensive library of machine learning models
designed to facilitate regression or classification tasks on a given
dataset. It encompasses a diverse range of models and provides a
comprehensive evaluation of each model's performance, delivering a
comprehensive set of metrics in a Python dictionary.
PURPOSE OF THE PACKAGE
======================
- The primary objective of the package is to offer a curated ensemble
of renowned scikit-learn models, enabling users to conveniently train
all models with a single function call.
FEATURES
========
- Collections of Machine learning models
- **Classification Models**
- 'LinearSVC'
- 'SGDClassifier'
- 'MLPClassifier'
- 'Perceptron'
- 'LogisticRegression'
- 'LogisticRegressionCV'
- 'SVC'
- 'CalibratedClassifierCV'
- 'PassiveAggressiveClassifier'
- 'LabelPropagation'
- 'LabelSpreading'
- 'RandomForestClassifier'
- 'GradientBoostingClassifier'
- 'QuadraticDiscriminantAnalysis'
- 'HistGradientBoostingClassifier'
- 'RidgeClassifierCV'
- 'RidgeClassifier'
- 'AdaBoostClassifier'
- 'ExtraTreesClassifier'
- 'KNeighborsClassifier'
- 'BaggingClassifier'
- 'BernoulliNB'
- 'LinearDiscriminantAnalysis'
- 'GaussianNB'
- 'NuSVC'
- 'DecisionTreeClassifier'
- 'NearestCentroid'
- 'ExtraTreeClassifier'
- 'DummyClassifier'
- **Regression Models**
- 'SVR'
- 'RandomForestRegressor'
- 'ExtraTreesRegressor'
- 'AdaBoostRegressor'
- 'NuSVR'
- 'GradientBoostingRegressor'
- 'KNeighborsRegressor'
- 'HuberRegressor'
- 'RidgeCV'
- 'BayesianRidge'
- 'Ridge'
- 'LinearRegression'
- 'LarsCV'
- 'MLPRegressor'
- 'XGBRegressor'
- 'CatBoostRegressor'
- 'LGBMRegressor'
- Can also be used for the custom models.
GETTING STARTED
===============
This package is available on PyPI, allowing for convenient installation through the PyPI repository.
Dependencies
============
::
- 'scikit-learn'
- 'xgboost'
- 'catboost'
- 'lightgbm'
INSTALLATION
============
If you already installed scikit-learn, the easiest way to install
modelLab is using ``pip``:
.. code:: bash
pip install modelLab
USAGE
=====
.. code:: python
>>> from modelLab import regressors,classifier
>>> regressors(X, y, models=models, verbose=False, rets=True) #X,y is data
>>> classifier(X, y, models=models, verbose=False, rets=True)
Examples
========
- Regression Problem
.. code:: python
>>> from modelLab import regressors
>>> from sklearn.datasets import fetch_california_housing
>>> X,y=fetch_california_housing(return_X_y=True)
>>> regressors(X,y,verbose=True)
Model: SVR
Adjusted R^2: -0.0249
R^2: -0.0229
MSE: 1.3768
RMSE: 1.1734
MAE: 0.8698
Model: RandomForestRegressor
Adjusted R^2: 0.8034
R^2: 0.8038
MSE: 0.2641
RMSE: 0.5139
MAE: 0.3364
Model: ExtraTreesRegressor
Adjusted R^2: 0.8102
R^2: 0.8105
MSE: 0.2550
RMSE: 0.5050
MAE: 0.3333
Model: AdaBoostRegressor
Adjusted R^2: 0.4563
R^2: 0.4574
MSE: 0.7304
RMSE: 0.8546
MAE: 0.7296
Model: NuSVR
Adjusted R^2: 0.0069
R^2: 0.0088
MSE: 1.3342
RMSE: 1.1551
MAE: 0.8803
Model: GradientBoostingRegressor
Adjusted R^2: 0.7753
R^2: 0.7757
MSE: 0.3019
RMSE: 0.5494
MAE: 0.3789
Model: KNeighborsRegressor
Adjusted R^2: 0.1435
R^2: 0.1451
MSE: 1.1506
RMSE: 1.0727
MAE: 0.8183
Model: HuberRegressor
Adjusted R^2: 0.3702
R^2: 0.3714
MSE: 0.8461
RMSE: 0.9198
MAE: 0.5800
Model: RidgeCV
Adjusted R^2: 0.5868
R^2: 0.5876
MSE: 0.5551
RMSE: 0.7450
MAE: 0.5423
Model: BayesianRidge
Adjusted R^2: 0.5868
R^2: 0.5876
MSE: 0.5551
RMSE: 0.7451
MAE: 0.5422
Model: Ridge
Adjusted R^2: 0.5867
R^2: 0.5875
MSE: 0.5552
RMSE: 0.7451
MAE: 0.5422
Model: LinearRegression
Adjusted R^2: 0.5867
R^2: 0.5875
MSE: 0.5552
RMSE: 0.7451
MAE: 0.5422
Model: LarsCV
Adjusted R^2: 0.5211
R^2: 0.5220
MSE: 0.6433
RMSE: 0.8021
MAE: 0.5524
Model: MLPRegressor
Adjusted R^2: -3.5120
R^2: -3.5032
MSE: 6.0613
RMSE: 2.4620
MAE: 1.7951
Model: XGBRegressor
Adjusted R^2: 0.8269
R^2: 0.8272
MSE: 0.2326
RMSE: 0.4822
MAE: 0.3195
Model: CatBoostRegressor
Adjusted R^2: 0.8461
R^2: 0.8464
MSE: 0.2068
RMSE: 0.4547
MAE: 0.3005
Model: LGBMRegressor
Adjusted R^2: 0.8319
R^2: 0.8322
MSE: 0.2259
RMSE: 0.4753
MAE: 0.3185
- Classification Problem
.. code:: python
>>> from modelLab import regressors,classifier
>>> from sklearn.datasets import load_iris
>>> X,y=load_iris(return_X_y=True)
>>> import warnings
>>> warnings.filterwarnings('ignore')
>>> classifier(X,y,verbose=True)
Model: LinearSVC
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: SGDClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9661
Model: MLPClassifier
Accuracy: 1.0000
Precision: 1.0000
Recall: 1.0000
F1 Score: 1.0000
Model: Perceptron
Accuracy: 0.8667
Precision: 0.9022
Recall: 0.8667
F1 Score: 0.8626
Model: LogisticRegression
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: SVC
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: CalibratedClassifierCV
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: PassiveAggressiveClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: LabelPropagation
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: LabelSpreading
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: RandomForestClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: GradientBoostingClassifier
Accuracy: 0.9333
Precision: 0.9436
Recall: 0.9333
F1 Score: 0.9331
Model: QuadraticDiscriminantAnalysis
Accuracy: 1.0000
Precision: 1.0000
Recall: 1.0000
F1 Score: 1.0000
Model: HistGradientBoostingClassifier
Accuracy: 0.9000
Precision: 0.9214
Recall: 0.9000
F1 Score: 0.8989
Model: RidgeClassifierCV
Accuracy: 0.8667
Precision: 0.8754
Recall: 0.8667
F1 Score: 0.8662
Model: RidgeClassifier
Accuracy: 0.8667
Precision: 0.8754
Recall: 0.8667
F1 Score: 0.8662
Model: AdaBoostClassifier
Accuracy: 0.9333
Precision: 0.9436
Recall: 0.9333
F1 Score: 0.9331
Model: ExtraTreesClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: KNeighborsClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: BaggingClassifier
Accuracy: 0.9333
Precision: 0.9436
Recall: 0.9333
F1 Score: 0.9331
Model: BernoulliNB
Accuracy: 0.2333
Precision: 0.0544
Recall: 0.2333
F1 Score: 0.0883
Model: LinearDiscriminantAnalysis
Accuracy: 1.0000
Precision: 1.0000
Recall: 1.0000
F1 Score: 1.0000
Model: GaussianNB
Accuracy: 0.9333
Precision: 0.9333
Recall: 0.9333
F1 Score: 0.9333
Model: NuSVC
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: DecisionTreeClassifier
Accuracy: 0.9333
Precision: 0.9436
Recall: 0.9333
F1 Score: 0.9331
Model: NearestCentroid
Accuracy: 0.9000
Precision: 0.9025
Recall: 0.9000
F1 Score: 0.9000
Model: ExtraTreeClassifier
Accuracy: 0.9667
Precision: 0.9694
Recall: 0.9667
F1 Score: 0.9667
Model: DummyClassifier
Accuracy: 0.2333
Precision: 0.0544
Recall: 0.2333
F1 Score: 0.0883
- Using Custom Models
.. code:: python
>>> from sklearn.datasets import make_regression
>>> from sklearn.linear_model import LinearRegression
>>> from modelLab import regressors
>>> X, y = make_regression(n_samples=100, n_features=10, random_state=42)
>>> models = {'Linear Regression': LinearRegression()}
>>> regressors(X, y, models=models, verbose=False, rets=True)
defaultdict(<class 'dict'>, {'Linear Regression': {'Adjusted R^2': 1.0, 'R^2': 1.0, 'MSE': 3.097635893749451e-26, 'RMSE': 1.7600101970583725e-13, 'MAE': 1.4992451724538115e-13}})
.. code:: python
>>> from sklearn.datasets import make_regression, make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from modelLab import classifier
>>> X, y = make_classification(n_samples=100, n_features=10, random_state=42)
>>> models = {'Logistic Regression': LogisticRegression()}
>>> classifier(X, y, models=models, verbose=False, rets=True)
defaultdict(<class 'dict'>, {'Logistic Regression': {'Accuracy': 0.95, 'Precision': 0.9545454545454545, 'Recall': 0.95, 'F1 Score': 0.949874686716792}})
Contributor and Author
======================
[**Abhishek Kaddipudi**]
`Mail`_
`Linkedin`_
`GitHub`_
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"description": ".. -*- mode: rst -*-\n\n|Version|_ |PythonVersion|_\n\n.. _Linkedin: https://www.linkedin.com/in/abhishek-kaddipudi-0b5183253\n.. _GitHub : https://github.com/Abhishekkaddipudi\n\n\n.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue\n.. _PythonVersion: https://pypi.org/project/modelLab/\n\n.. |Version| image:: https://img.shields.io/badge/Version-V0.1-blue\n.. _Version: https://github.com/Abhishekkaddipudi/modelLab\n\n.. |Unit_Test| image:: https://github.com/Abhishekkaddipudi/modelLab/actions/workflows/main.yml/badge.svg\n.. _Unit_Test: https://github.com/Abhishekkaddipudi/modelLab\n\n.. _Mail: mailto:abhishekkaddipudi123@gmail.com\n\n**modelLab** is a comprehensive library of machine learning models\ndesigned to facilitate regression or classification tasks on a given\ndataset. It encompasses a diverse range of models and provides a\ncomprehensive evaluation of each model's performance, delivering a\ncomprehensive set of metrics in a Python dictionary.\n\nPURPOSE OF THE PACKAGE\n======================\n\n- The primary objective of the package is to offer a curated ensemble\n of renowned scikit-learn models, enabling users to conveniently train\n all models with a single function call.\n\nFEATURES\n========\n\n- Collections of Machine learning models\n\n - **Classification Models**\n\n - 'LinearSVC'\n - 'SGDClassifier'\n - 'MLPClassifier'\n - 'Perceptron'\n - 'LogisticRegression'\n - 'LogisticRegressionCV'\n - 'SVC'\n - 'CalibratedClassifierCV'\n - 'PassiveAggressiveClassifier'\n - 'LabelPropagation'\n - 'LabelSpreading'\n - 'RandomForestClassifier'\n - 'GradientBoostingClassifier'\n - 'QuadraticDiscriminantAnalysis'\n - 'HistGradientBoostingClassifier'\n - 'RidgeClassifierCV'\n - 'RidgeClassifier'\n - 'AdaBoostClassifier'\n - 'ExtraTreesClassifier'\n - 'KNeighborsClassifier'\n - 'BaggingClassifier'\n - 'BernoulliNB'\n - 'LinearDiscriminantAnalysis'\n - 'GaussianNB'\n - 'NuSVC'\n - 'DecisionTreeClassifier'\n - 'NearestCentroid'\n - 'ExtraTreeClassifier'\n - 'DummyClassifier'\n\n - **Regression Models**\n\n - 'SVR'\n - 'RandomForestRegressor'\n - 'ExtraTreesRegressor'\n - 'AdaBoostRegressor'\n - 'NuSVR'\n - 'GradientBoostingRegressor'\n - 'KNeighborsRegressor'\n - 'HuberRegressor'\n - 'RidgeCV'\n - 'BayesianRidge'\n - 'Ridge'\n - 'LinearRegression'\n - 'LarsCV'\n - 'MLPRegressor'\n - 'XGBRegressor'\n - 'CatBoostRegressor'\n - 'LGBMRegressor'\n\n- Can also be used for the custom models.\n\nGETTING STARTED \n===============\n\nThis package is available on PyPI, allowing for convenient installation through the PyPI repository.\n\nDependencies\n============\n\n::\n\n - 'scikit-learn'\n - 'xgboost'\n - 'catboost'\n - 'lightgbm'\n\nINSTALLATION\n============\n\nIf you already installed scikit-learn, the easiest way to install\nmodelLab is using ``pip``:\n\n.. code:: bash\n\n pip install modelLab\n\nUSAGE\n=====\n\n.. code:: python\n\n >>> from modelLab import regressors,classifier\n >>> regressors(X, y, models=models, verbose=False, rets=True) #X,y is data\n >>> classifier(X, y, models=models, verbose=False, rets=True)\n\nExamples\n========\n\n- Regression Problem\n\n.. code:: python\n\n >>> from modelLab import regressors\n >>> from sklearn.datasets import fetch_california_housing\n >>> X,y=fetch_california_housing(return_X_y=True)\n >>> regressors(X,y,verbose=True)\n Model: SVR\n Adjusted R^2: -0.0249\n R^2: -0.0229\n MSE: 1.3768\n RMSE: 1.1734\n MAE: 0.8698\n\n Model: RandomForestRegressor\n Adjusted R^2: 0.8034\n R^2: 0.8038\n MSE: 0.2641\n RMSE: 0.5139\n MAE: 0.3364\n\n Model: ExtraTreesRegressor\n Adjusted R^2: 0.8102\n R^2: 0.8105\n MSE: 0.2550\n RMSE: 0.5050\n MAE: 0.3333\n\n Model: AdaBoostRegressor\n Adjusted R^2: 0.4563\n R^2: 0.4574\n MSE: 0.7304\n RMSE: 0.8546\n MAE: 0.7296\n\n Model: NuSVR\n Adjusted R^2: 0.0069\n R^2: 0.0088\n MSE: 1.3342\n RMSE: 1.1551\n MAE: 0.8803\n\n Model: GradientBoostingRegressor\n Adjusted R^2: 0.7753\n R^2: 0.7757\n MSE: 0.3019\n RMSE: 0.5494\n MAE: 0.3789\n\n Model: KNeighborsRegressor\n Adjusted R^2: 0.1435\n R^2: 0.1451\n MSE: 1.1506\n RMSE: 1.0727\n MAE: 0.8183\n\n Model: HuberRegressor\n Adjusted R^2: 0.3702\n R^2: 0.3714\n MSE: 0.8461\n RMSE: 0.9198\n MAE: 0.5800\n\n Model: RidgeCV\n Adjusted R^2: 0.5868\n R^2: 0.5876\n MSE: 0.5551\n RMSE: 0.7450\n MAE: 0.5423\n\n Model: BayesianRidge\n Adjusted R^2: 0.5868\n R^2: 0.5876\n MSE: 0.5551\n RMSE: 0.7451\n MAE: 0.5422\n\n Model: Ridge\n Adjusted R^2: 0.5867\n R^2: 0.5875\n MSE: 0.5552\n RMSE: 0.7451\n MAE: 0.5422\n\n Model: LinearRegression\n Adjusted R^2: 0.5867\n R^2: 0.5875\n MSE: 0.5552\n RMSE: 0.7451\n MAE: 0.5422\n\n Model: LarsCV\n Adjusted R^2: 0.5211\n R^2: 0.5220\n MSE: 0.6433\n RMSE: 0.8021\n MAE: 0.5524\n\n Model: MLPRegressor\n Adjusted R^2: -3.5120\n R^2: -3.5032\n MSE: 6.0613\n RMSE: 2.4620\n MAE: 1.7951\n\n Model: XGBRegressor\n Adjusted R^2: 0.8269\n R^2: 0.8272\n MSE: 0.2326\n RMSE: 0.4822\n MAE: 0.3195\n\n Model: CatBoostRegressor\n Adjusted R^2: 0.8461\n R^2: 0.8464\n MSE: 0.2068\n RMSE: 0.4547\n MAE: 0.3005\n\n Model: LGBMRegressor\n Adjusted R^2: 0.8319\n R^2: 0.8322\n MSE: 0.2259\n RMSE: 0.4753\n MAE: 0.3185\n\n- Classification Problem\n\n.. code:: python\n\n >>> from modelLab import regressors,classifier\n >>> from sklearn.datasets import load_iris\n >>> X,y=load_iris(return_X_y=True)\n >>> import warnings \n >>> warnings.filterwarnings('ignore')\n >>> classifier(X,y,verbose=True) \n Model: LinearSVC\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: SGDClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9661\n\n Model: MLPClassifier\n Accuracy: 1.0000\n Precision: 1.0000\n Recall: 1.0000\n F1 Score: 1.0000\n\n Model: Perceptron\n Accuracy: 0.8667\n Precision: 0.9022\n Recall: 0.8667\n F1 Score: 0.8626\n\n Model: LogisticRegression\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: SVC\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: CalibratedClassifierCV\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: PassiveAggressiveClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: LabelPropagation\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: LabelSpreading\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: RandomForestClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: GradientBoostingClassifier\n Accuracy: 0.9333\n Precision: 0.9436\n Recall: 0.9333\n F1 Score: 0.9331\n\n Model: QuadraticDiscriminantAnalysis\n Accuracy: 1.0000\n Precision: 1.0000\n Recall: 1.0000\n F1 Score: 1.0000\n\n Model: HistGradientBoostingClassifier\n Accuracy: 0.9000\n Precision: 0.9214\n Recall: 0.9000\n F1 Score: 0.8989\n\n Model: RidgeClassifierCV\n Accuracy: 0.8667\n Precision: 0.8754\n Recall: 0.8667\n F1 Score: 0.8662\n\n Model: RidgeClassifier\n Accuracy: 0.8667\n Precision: 0.8754\n Recall: 0.8667\n F1 Score: 0.8662\n\n Model: AdaBoostClassifier\n Accuracy: 0.9333\n Precision: 0.9436\n Recall: 0.9333\n F1 Score: 0.9331\n\n Model: ExtraTreesClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: KNeighborsClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: BaggingClassifier\n Accuracy: 0.9333\n Precision: 0.9436\n Recall: 0.9333\n F1 Score: 0.9331\n\n Model: BernoulliNB\n Accuracy: 0.2333\n Precision: 0.0544\n Recall: 0.2333\n F1 Score: 0.0883\n\n Model: LinearDiscriminantAnalysis\n Accuracy: 1.0000\n Precision: 1.0000\n Recall: 1.0000\n F1 Score: 1.0000\n\n Model: GaussianNB\n Accuracy: 0.9333\n Precision: 0.9333\n Recall: 0.9333\n F1 Score: 0.9333\n\n Model: NuSVC\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: DecisionTreeClassifier\n Accuracy: 0.9333\n Precision: 0.9436\n Recall: 0.9333\n F1 Score: 0.9331\n\n Model: NearestCentroid\n Accuracy: 0.9000\n Precision: 0.9025\n Recall: 0.9000\n F1 Score: 0.9000\n\n Model: ExtraTreeClassifier\n Accuracy: 0.9667\n Precision: 0.9694\n Recall: 0.9667\n F1 Score: 0.9667\n\n Model: DummyClassifier\n Accuracy: 0.2333\n Precision: 0.0544\n Recall: 0.2333\n F1 Score: 0.0883\n\n- Using Custom Models\n\n.. code:: python\n\n >>> from sklearn.datasets import make_regression\n >>> from sklearn.linear_model import LinearRegression\n >>> from modelLab import regressors\n >>> X, y = make_regression(n_samples=100, n_features=10, random_state=42)\n >>> models = {'Linear Regression': LinearRegression()}\n >>> regressors(X, y, models=models, verbose=False, rets=True)\n defaultdict(<class 'dict'>, {'Linear Regression': {'Adjusted R^2': 1.0, 'R^2': 1.0, 'MSE': 3.097635893749451e-26, 'RMSE': 1.7600101970583725e-13, 'MAE': 1.4992451724538115e-13}})\n\n.. code:: python\n\n >>> from sklearn.datasets import make_regression, make_classification\n >>> from sklearn.linear_model import LogisticRegression\n >>> from modelLab import classifier\n >>> X, y = make_classification(n_samples=100, n_features=10, random_state=42)\n >>> models = {'Logistic Regression': LogisticRegression()} \n >>> classifier(X, y, models=models, verbose=False, rets=True)\n defaultdict(<class 'dict'>, {'Logistic Regression': {'Accuracy': 0.95, 'Precision': 0.9545454545454545, 'Recall': 0.95, 'F1 Score': 0.949874686716792}})\n\n\nContributor and Author\n======================\n [**Abhishek Kaddipudi**]\n\n `Mail`_ \n \n `Linkedin`_\n\n `GitHub`_\n",
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