Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Project description
Lazy Predict
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.
Usage
To use Lazy Predict in a project:
import lazypredict
Classification
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) 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 |
Regression
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) | 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 |
History
0.2.7 (2020-07-09)
Removed catboost regressor and classifier
0.2.6 (2020-01-22)
Added xgboost, lightgbm, catboost regressors and classifiers
0.2.5 (2020-01-20)
Removed troublesome regressors from list of CLASSIFIERS
0.2.4 (2020-01-19)
Removed troublesome regressors from list of REGRESSORS
Added feature to input custom metric for evaluation
Added feature to return predictions as dataframe
Added model training time for each model
0.2.3 (2019-11-22)
Removed TheilSenRegressor from list of REGRESSORS
Removed GaussianProcessClassifier from list of CLASSIFIERS
0.2.2 (2019-11-18)
Fixed automatic deployment issue.
0.2.1 (2019-11-18)
Release of Regression feature.
0.2.0 (2019-11-17)
Release of Classification feature.
0.1.0 (2019-11-16)
First release on PyPI.
Project details
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