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‎README.md‎

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[![Downloads](https://pepy.tech/badge/lazypredict)](https://pepy.tech/project/lazypredict)
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[![CodeFactor](https://www.codefactor.io/repository/github/shankarpandala/lazypredict/badge)](https://www.codefactor.io/repository/github/shankarpandala/lazypredict)
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Lazy Predict helps build a lot of basic models without much code and
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helps understand which models works better without any parameter tuning.
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Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning.
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- Free software: MIT license
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- Documentation: <https://lazypredict.readthedocs.io>.
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- Free software: MIT license
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- Documentation: <https://lazypredict.readthedocs.io>
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# Installation
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## Installation
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To install Lazy Predict:
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pip install lazypredict
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```bash
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pip install lazypredict
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```
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# Usage
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## Usage
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To use Lazy Predict in a project:
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import lazypredict
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# Classification
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Example :
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from lazypredict.Supervised import LazyClassifier
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from sklearn.datasets import load_breast_cancer
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from sklearn.model_selection import train_test_split
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data = load_breast_cancer()
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X = data.data
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y= data.target
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X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
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clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
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models,predictions = clf.fit(X_train, X_test, y_train, y_test)
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print(models)
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| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
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|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
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| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
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| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
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| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
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| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
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| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
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| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
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| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
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| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
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| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
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| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
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| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
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| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
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| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
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| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
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| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
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| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
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| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
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| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
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| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
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| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
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| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
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| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
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| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
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| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
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| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
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| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
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| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
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| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
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| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
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| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
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# Regression
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Example :
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from lazypredict.Supervised import LazyRegressor
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from sklearn import datasets
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from sklearn.utils import shuffle
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import numpy as np
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boston = datasets.load_boston()
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X, y = shuffle(boston.data, boston.target, random_state=13)
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X = X.astype(np.float32)
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offset = int(X.shape[0] * 0.9)
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X_train, y_train = X[:offset], y[:offset]
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X_test, y_test = X[offset:], y[offset:]
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reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
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models, predictions = reg.fit(X_train, X_test, y_train, y_test)
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print(models)
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| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
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|:------------------------------|-------------------:|----------:|------:|-----------:|
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| SVR | 0.83 | 0.88 | 2.62 | 0.01 |
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| BaggingRegressor | 0.83 | 0.88 | 2.63 | 0.03 |
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| NuSVR | 0.82 | 0.86 | 2.76 | 0.03 |
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| RandomForestRegressor | 0.81 | 0.86 | 2.78 | 0.21 |
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| XGBRegressor | 0.81 | 0.86 | 2.79 | 0.06 |
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| GradientBoostingRegressor | 0.81 | 0.86 | 2.84 | 0.11 |
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| ExtraTreesRegressor | 0.79 | 0.84 | 2.98 | 0.12 |
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| AdaBoostRegressor | 0.78 | 0.83 | 3.04 | 0.07 |
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| HistGradientBoostingRegressor | 0.77 | 0.83 | 3.06 | 0.17 |
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| PoissonRegressor | 0.77 | 0.83 | 3.11 | 0.01 |
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| LGBMRegressor | 0.77 | 0.83 | 3.11 | 0.07 |
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| KNeighborsRegressor | 0.77 | 0.83 | 3.12 | 0.01 |
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| DecisionTreeRegressor | 0.65 | 0.74 | 3.79 | 0.01 |
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| MLPRegressor | 0.65 | 0.74 | 3.80 | 1.63 |
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| HuberRegressor | 0.64 | 0.74 | 3.84 | 0.01 |
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| GammaRegressor | 0.64 | 0.73 | 3.88 | 0.01 |
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| LinearSVR | 0.62 | 0.72 | 3.96 | 0.01 |
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| RidgeCV | 0.62 | 0.72 | 3.97 | 0.01 |
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| BayesianRidge | 0.62 | 0.72 | 3.97 | 0.01 |
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| Ridge | 0.62 | 0.72 | 3.97 | 0.01 |
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| TransformedTargetRegressor | 0.62 | 0.72 | 3.97 | 0.01 |
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| LinearRegression | 0.62 | 0.72 | 3.97 | 0.01 |
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| ElasticNetCV | 0.62 | 0.72 | 3.98 | 0.04 |
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| LassoCV | 0.62 | 0.72 | 3.98 | 0.06 |
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| LassoLarsIC | 0.62 | 0.72 | 3.98 | 0.01 |
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| LassoLarsCV | 0.62 | 0.72 | 3.98 | 0.02 |
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| Lars | 0.61 | 0.72 | 3.99 | 0.01 |
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| LarsCV | 0.61 | 0.71 | 4.02 | 0.04 |
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| SGDRegressor | 0.60 | 0.70 | 4.07 | 0.01 |
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| TweedieRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
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| GeneralizedLinearRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
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| ElasticNet | 0.58 | 0.69 | 4.16 | 0.01 |
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| Lasso | 0.54 | 0.66 | 4.35 | 0.02 |
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| RANSACRegressor | 0.53 | 0.65 | 4.41 | 0.04 |
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| OrthogonalMatchingPursuitCV | 0.45 | 0.59 | 4.78 | 0.02 |
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| PassiveAggressiveRegressor | 0.37 | 0.54 | 5.09 | 0.01 |
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| GaussianProcessRegressor | 0.23 | 0.43 | 5.65 | 0.03 |
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| OrthogonalMatchingPursuit | 0.16 | 0.38 | 5.89 | 0.01 |
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| ExtraTreeRegressor | 0.08 | 0.32 | 6.17 | 0.01 |
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| DummyRegressor | -0.38 | -0.02 | 7.56 | 0.01 |
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| LassoLars | -0.38 | -0.02 | 7.56 | 0.01 |
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| KernelRidge | -11.50 | -8.25 | 22.74 | 0.01 |
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```python
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import lazypredict
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```
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## Classification
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Example:
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```python
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from lazypredict.Supervised import LazyClassifier
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from sklearn.datasets import load_breast_cancer
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from sklearn.model_selection import train_test_split
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data = load_breast_cancer()
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X = data.data
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y = data.target
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=123)
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clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
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models, predictions = clf.fit(X_train, X_test, y_train, y_test)
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print(models)
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```
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| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
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|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
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| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
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| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
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| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
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| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
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| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
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| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
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| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
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| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
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| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
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| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
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| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
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| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
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| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
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| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
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| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
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| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
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| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
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| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
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| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
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| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
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| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
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| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
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| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
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| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
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| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
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| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
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| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
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| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
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| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
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| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
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## Regression
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Example:
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```python
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from lazypredict.Supervised import LazyRegressor
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from sklearn import datasets
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from sklearn.utils import shuffle
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import numpy as np
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boston = datasets.load_boston()
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X, y = shuffle(boston.data, boston.target, random_state=13)
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X = X.astype(np.float32)
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offset = int(X.shape[0] * 0.9)
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X_train, y_train = X[:offset], y[:offset]
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X_test, y_test = X[offset:], y[offset:]
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reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
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models, predictions = reg.fit(X_train, X_test, y_train, y_test)
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print(models)
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```
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| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
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|:------------------------------|-------------------:|----------:|------:|-----------:|
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| SVR | 0.83 | 0.88 | 2.62 | 0.01 |
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| BaggingRegressor | 0.83 | 0.88 | 2.63 | 0.03 |
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| NuSVR | 0.82 | 0.86 | 2.76 | 0.03 |
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| RandomForestRegressor | 0.81 | 0.86 | 2.78 | 0.21 |
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| XGBRegressor | 0.81 | 0.86 | 2.79 | 0.06 |
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| GradientBoostingRegressor | 0.81 | 0.86 | 2.84 | 0.11 |
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| ExtraTreesRegressor | 0.79 | 0.84 | 2.98 | 0.12 |
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| AdaBoostRegressor | 0.78 | 0.83 | 3.04 | 0.07 |
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| HistGradientBoostingRegressor | 0.77 | 0.83 | 3.06 | 0.17 |
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| PoissonRegressor | 0.77 | 0.83 | 3.11 | 0.01 |
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| LGBMRegressor | 0.77 | 0.83 | 3.11 | 0.07 |
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| KNeighborsRegressor | 0.77 | 0.83 | 3.12 | 0.01 |
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| DecisionTreeRegressor | 0.65 | 0.74 | 3.79 | 0.01 |
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| MLPRegressor | 0.65 | 0.74 | 3.80 | 1.63 |
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| HuberRegressor | 0.64 | 0.74 | 3.84 | 0.01 |
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| GammaRegressor | 0.64 | 0.73 | 3.88 | 0.01 |
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| LinearSVR | 0.62 | 0.72 | 3.96 | 0.01 |
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| RidgeCV | 0.62 | 0.72 | 3.97 | 0.01 |
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| BayesianRidge | 0.62 | 0.72 | 3.97 | 0.01 |
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| Ridge | 0.62 | 0.72 | 3.97 | 0.01 |
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| TransformedTargetRegressor | 0.62 | 0.72 | 3.97 | 0.01 |
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| LinearRegression | 0.62 | 0.72 | 3.97 | 0.01 |
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| ElasticNetCV | 0.62 | 0.72 | 3.98 | 0.04 |
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| LassoCV | 0.62 | 0.72 | 3.98 | 0.06 |
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| LassoLarsIC | 0.62 | 0.72 | 3.98 | 0.01 |
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| LassoLarsCV | 0.62 | 0.72 | 3.98 | 0.02 |
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| Lars | 0.61 | 0.72 | 3.99 | 0.01 |
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| LarsCV | 0.61 | 0.71 | 4.02 | 0.04 |
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| SGDRegressor | 0.60 | 0.70 | 4.07 | 0.01 |
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| TweedieRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
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| GeneralizedLinearRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
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| ElasticNet | 0.58 | 0.69 | 4.16 | 0.01 |
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| Lasso | 0.54 | 0.66 | 4.35 | 0.02 |
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| RANSACRegressor | 0.53 | 0.65 | 4.41 | 0.04 |
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| OrthogonalMatchingPursuitCV | 0.45 | 0.59 | 4.78 | 0.02 |
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| PassiveAggressiveRegressor | 0.37 | 0.54 | 5.09 | 0.01 |
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| GaussianProcessRegressor | 0.23 | 0.43 | 5.65 | 0.03 |
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| OrthogonalMatchingPursuit | 0.16 | 0.38 | 5.89 | 0.01 |
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| ExtraTreeRegressor | 0.08 | 0.32 | 6.17 | 0.01 |
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| DummyRegressor | -0.38 | -0.02 | 7.56 | 0.01 |
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| LassoLars | -0.38 | -0.02 | 7.56 | 0.01 |
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| KernelRidge | -11.50 | -8.25 | 22.74 | 0.01 |

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