Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
- Free software: MIT license
- Documentation: https://lazypredict.readthedocs.io.
To use Lazy Predict in a project:
import lazypredict
Example
from lazypredict.Supervised import LazyClassifier from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split data = load_breast_cancer() X = data.data y= data.target X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123) clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None) models,predictions = clf.fit(X_train, X_test, y_train, y_test) print(models) | Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken | |:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:| | LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 | | SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 | | MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 | | Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 | | LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 | | LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 | | SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 | | CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 | | PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 | | LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 | | LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 | | RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 | | GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 | | QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 | | HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 | | RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 | | RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 | | AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 | | ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 | | KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 | | BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 | | BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 | | LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 | | GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 | | NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 | | DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 | | NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 | | ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 | | CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 | | DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
Example
from lazypredict.Supervised import LazyRegressor from sklearn import datasets from sklearn.utils import shuffle import numpy as np boston = datasets.load_boston() X, y = shuffle(boston.data, boston.target, random_state=13) X = X.astype(np.float32) offset = int(X.shape[0] * 0.9) X_train, y_train = X[:offset], y[:offset] X_test, y_test = X[offset:], y[offset:] reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None) models,predictions = reg.fit(X_train, X_test, y_train, y_test) print(models) | Model | R-Squared | RMSE | Time Taken | |:------------------------------|------------:|---------:|-------------:| | SVR | 0.877199 | 2.62054 | 0.0330021 | | RandomForestRegressor | 0.874429 | 2.64993 | 0.0659981 | | ExtraTreesRegressor | 0.867566 | 2.72138 | 0.0570002 | | AdaBoostRegressor | 0.865851 | 2.73895 | 0.144999 | | NuSVR | 0.863712 | 2.7607 | 0.0340044 | | GradientBoostingRegressor | 0.858693 | 2.81107 | 0.13 | | KNeighborsRegressor | 0.826307 | 3.1166 | 0.0179954 | | HistGradientBoostingRegressor | 0.810479 | 3.25551 | 0.820995 | | BaggingRegressor | 0.800056 | 3.34383 | 0.0579946 | | MLPRegressor | 0.750536 | 3.73503 | 0.725997 | | HuberRegressor | 0.736973 | 3.83522 | 0.0370018 | | LinearSVR | 0.71914 | 3.9631 | 0.0179989 | | RidgeCV | 0.718402 | 3.9683 | 0.018003 | | BayesianRidge | 0.718102 | 3.97041 | 0.0159984 | | Ridge | 0.71765 | 3.9736 | 0.0149941 | | LinearRegression | 0.71753 | 3.97444 | 0.0190051 | | TransformedTargetRegressor | 0.71753 | 3.97444 | 0.012001 | | LassoCV | 0.717337 | 3.9758 | 0.0960066 | | ElasticNetCV | 0.717104 | 3.97744 | 0.0860076 | | LassoLarsCV | 0.717045 | 3.97786 | 0.0490005 | | LassoLarsIC | 0.716636 | 3.98073 | 0.0210001 | | LarsCV | 0.715031 | 3.99199 | 0.0450008 | | Lars | 0.715031 | 3.99199 | 0.0269964 | | SGDRegressor | 0.714362 | 3.99667 | 0.0210009 | | RANSACRegressor | 0.707849 | 4.04198 | 0.111998 | | ElasticNet | 0.690408 | 4.16088 | 0.0190012 | | Lasso | 0.662141 | 4.34668 | 0.0180018 | | OrthogonalMatchingPursuitCV | 0.591632 | 4.77877 | 0.0180008 | | ExtraTreeRegressor | 0.583314 | 4.82719 | 0.0129974 | | PassiveAggressiveRegressor | 0.556668 | 4.97914 | 0.0150032 | | GaussianProcessRegressor | 0.428298 | 5.65425 | 0.0580051 | | OrthogonalMatchingPursuit | 0.379295 | 5.89159 | 0.0180039 | | DecisionTreeRegressor | 0.318767 | 6.17217 | 0.0230272 | | DummyRegressor | -0.0215752 | 7.55832 | 0.0140116 | | LassoLars | -0.0215752 | 7.55832 | 0.0180008 | | KernelRidge | -8.24669 | 22.7396 | 0.0309792 |
Warning
Regression and Classification are replaced with LazyRegressor and LazyClassifier. Regression and Classification classes will be removed in next release