Lazy Predict Regression Auto SkLearn
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 |