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 |
Warning
Regression and Classification are replaced with LazyRegressor and LazyClassifier. Regression and Classification classes will be removed in next release