Package: lazypredict
============
Lazy Predict.. image::https://img.shields.io/pypi/v/lazypredict.svg:target:https://pypi.python.org/pypi/lazypredict.. image::https://img.shields.io/travis/shankarpandala/lazypredict.svg:target:https://travis-ci.org/shankarpandala/lazypredict.. image::https://readthedocs.org/projects/lazypredict/badge/?version=latest:target:https://lazypredict.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image::https://pepy.tech/badge/lazypredict:target:https://pepy.tech/project/lazypredict:alt: Downloads.. image::https://www.codefactor.io/repository/github/shankarpandala/lazypredict/badge:target:https://www.codefactor.io/repository/github/shankarpandala/lazypredict:alt: CodeFactorLazy Predict helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.Free software: MIT licenseDocumentation:https://lazypredict.readthedocs.io.============
InstallationTo install Lazy Predict::pip install lazypredict=====
UsageTo use Lazy Predict in a project::import lazypredict==============
ClassificationExample ::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 |==========
RegressionExample ::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                         | Adjusted R-Squared | R-Squared |  RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| SVR                           |               0.83 |      0.88 |  2.62 |       0.01 |
| BaggingRegressor              |               0.83 |      0.88 |  2.63 |       0.03 |
| NuSVR                         |               0.82 |      0.86 |  2.76 |       0.03 |
| RandomForestRegressor         |               0.81 |      0.86 |  2.78 |       0.21 |
| XGBRegressor                  |               0.81 |      0.86 |  2.79 |       0.06 |
| GradientBoostingRegressor     |               0.81 |      0.86 |  2.84 |       0.11 |
| ExtraTreesRegressor           |               0.79 |      0.84 |  2.98 |       0.12 |
| AdaBoostRegressor             |               0.78 |      0.83 |  3.04 |       0.07 |
| HistGradientBoostingRegressor |               0.77 |      0.83 |  3.06 |       0.17 |
| PoissonRegressor              |               0.77 |      0.83 |  3.11 |       0.01 |
| LGBMRegressor                 |               0.77 |      0.83 |  3.11 |       0.07 |
| KNeighborsRegressor           |               0.77 |      0.83 |  3.12 |       0.01 |
| DecisionTreeRegressor         |               0.65 |      0.74 |  3.79 |       0.01 |
| MLPRegressor                  |               0.65 |      0.74 |  3.80 |       1.63 |
| HuberRegressor                |               0.64 |      0.74 |  3.84 |       0.01 |
| GammaRegressor                |               0.64 |      0.73 |  3.88 |       0.01 |
| LinearSVR                     |               0.62 |      0.72 |  3.96 |       0.01 |
| RidgeCV                       |               0.62 |      0.72 |  3.97 |       0.01 |
| BayesianRidge                 |               0.62 |      0.72 |  3.97 |       0.01 |
| Ridge                         |               0.62 |      0.72 |  3.97 |       0.01 |
| TransformedTargetRegressor    |               0.62 |      0.72 |  3.97 |       0.01 |
| LinearRegression              |               0.62 |      0.72 |  3.97 |       0.01 |
| ElasticNetCV                  |               0.62 |      0.72 |  3.98 |       0.04 |
| LassoCV                       |               0.62 |      0.72 |  3.98 |       0.06 |
| LassoLarsIC                   |               0.62 |      0.72 |  3.98 |       0.01 |
| LassoLarsCV                   |               0.62 |      0.72 |  3.98 |       0.02 |
| Lars                          |               0.61 |      0.72 |  3.99 |       0.01 |
| LarsCV                        |               0.61 |      0.71 |  4.02 |       0.04 |
| SGDRegressor                  |               0.60 |      0.70 |  4.07 |       0.01 |
| TweedieRegressor              |               0.59 |      0.70 |  4.12 |       0.01 |
| GeneralizedLinearRegressor    |               0.59 |      0.70 |  4.12 |       0.01 |
| ElasticNet                    |               0.58 |      0.69 |  4.16 |       0.01 |
| Lasso                         |               0.54 |      0.66 |  4.35 |       0.02 |
| RANSACRegressor               |               0.53 |      0.65 |  4.41 |       0.04 |
| OrthogonalMatchingPursuitCV   |               0.45 |      0.59 |  4.78 |       0.02 |
| PassiveAggressiveRegressor    |               0.37 |      0.54 |  5.09 |       0.01 |
| GaussianProcessRegressor      |               0.23 |      0.43 |  5.65 |       0.03 |
| OrthogonalMatchingPursuit     |               0.16 |      0.38 |  5.89 |       0.01 |
| ExtraTreeRegressor            |               0.08 |      0.32 |  6.17 |       0.01 |
| DummyRegressor                |              -0.38 |     -0.02 |  7.56 |       0.01 |
| LassoLars                     |              -0.38 |     -0.02 |  7.56 |       0.01 |
| KernelRidge                   |             -11.50 |     -8.25 | 22.74 |       0.01 |.. warning::
Regression and Classification are replaced with LazyRegressor and LazyClassifier.
Regression and Classification classes will be removed in next release=======
History0.2.11 (2022-02-06)Updated the default version to 3.90.2.10 (2022-02-06)Fixed issue with older version of Scikit-learnReduced dependencies sctrictly to few0.2.8 (2021-02-06)Removed StackingRegressor and CheckingClassifier.Added provided_models method.Added adjusted r-squared metric.Added cardinality check to split categorical columns into low and high cardinality features.Added different transformation pipeline for low and high cardinality features.Included all number dtypes as inputs.Fixed dependencies.Improved documentation.0.2.7 (2020-07-09)Removed catboost regressor and classifier0.2.6 (2020-01-22)Added xgboost, lightgbm, catboost regressors and classifiers0.2.5 (2020-01-20)Removed troublesome regressors from list of CLASSIFIERS0.2.4 (2020-01-19)Removed troublesome regressors from list of REGRESSORSAdded feature to input custom metric for evaluationAdded feature to return predictions as dataframeAdded model training time for each model0.2.3 (2019-11-22)Removed TheilSenRegressor from list of REGRESSORSRemoved GaussianProcessClassifier from list of CLASSIFIERS0.2.2 (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.