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:
pip install modelLabUSAGE
>>> 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
>>> 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
>>> 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
>>> 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}})>>> 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 |
