lazy predict regression auto sklearn

1from lazypredict.Supervised import LazyRegressor
2from sklearn import datasets
3from sklearn.utils import shuffle
4import numpy as np
5
6boston = datasets.load_boston()
7X, y = shuffle(boston.data, boston.target, random_state=13)
8X = X.astype(np.float32)
9
10offset = int(X.shape[0] * 0.9)
11
12X_train, y_train = X[:offset], y[:offset]
13X_test, y_test = X[offset:], y[offset:]
14
15reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
16models, predictions = reg.fit(X_train, X_test, y_train, y_test)
17
18print(models)
19
20
21| Model                         | Adjusted R-Squared | R-Squared |  RMSE | Time Taken |
22|:------------------------------|-------------------:|----------:|------:|-----------:|
23| SVR                           |               0.83 |      0.88 |  2.62 |       0.01 |
24| BaggingRegressor              |               0.83 |      0.88 |  2.63 |       0.03 |
25| NuSVR                         |               0.82 |      0.86 |  2.76 |       0.03 |
26| RandomForestRegressor         |               0.81 |      0.86 |  2.78 |       0.21 |
27| XGBRegressor                  |               0.81 |      0.86 |  2.79 |       0.06 |
28| GradientBoostingRegressor     |               0.81 |      0.86 |  2.84 |       0.11 |
29| ExtraTreesRegressor           |               0.79 |      0.84 |  2.98 |       0.12 |
30| AdaBoostRegressor             |               0.78 |      0.83 |  3.04 |       0.07 |
31| HistGradientBoostingRegressor |               0.77 |      0.83 |  3.06 |       0.17 |
32| PoissonRegressor              |               0.77 |      0.83 |  3.11 |       0.01 |
33| LGBMRegressor                 |               0.77 |      0.83 |  3.11 |       0.07 |
34| KNeighborsRegressor           |               0.77 |      0.83 |  3.12 |       0.01 |
35| DecisionTreeRegressor         |               0.65 |      0.74 |  3.79 |       0.01 |
36| MLPRegressor                  |               0.65 |      0.74 |  3.80 |       1.63 |
37| HuberRegressor                |               0.64 |      0.74 |  3.84 |       0.01 |
38| GammaRegressor                |               0.64 |      0.73 |  3.88 |       0.01 |
39| LinearSVR                     |               0.62 |      0.72 |  3.96 |       0.01 |
40| RidgeCV                       |               0.62 |      0.72 |  3.97 |       0.01 |
41| BayesianRidge                 |               0.62 |      0.72 |  3.97 |       0.01 |
42| Ridge                         |               0.62 |      0.72 |  3.97 |       0.01 |
43| TransformedTargetRegressor    |               0.62 |      0.72 |  3.97 |       0.01 |
44| LinearRegression              |               0.62 |      0.72 |  3.97 |       0.01 |
45| ElasticNetCV                  |               0.62 |      0.72 |  3.98 |       0.04 |
46| LassoCV                       |               0.62 |      0.72 |  3.98 |       0.06 |
47| LassoLarsIC                   |               0.62 |      0.72 |  3.98 |       0.01 |
48| LassoLarsCV                   |               0.62 |      0.72 |  3.98 |       0.02 |
49| Lars                          |               0.61 |      0.72 |  3.99 |       0.01 |
50| LarsCV                        |               0.61 |      0.71 |  4.02 |       0.04 |
51| SGDRegressor                  |               0.60 |      0.70 |  4.07 |       0.01 |
52| TweedieRegressor              |               0.59 |      0.70 |  4.12 |       0.01 |
53| GeneralizedLinearRegressor    |               0.59 |      0.70 |  4.12 |       0.01 |
54| ElasticNet                    |               0.58 |      0.69 |  4.16 |       0.01 |
55| Lasso                         |               0.54 |      0.66 |  4.35 |       0.02 |
56| RANSACRegressor               |               0.53 |      0.65 |  4.41 |       0.04 |
57| OrthogonalMatchingPursuitCV   |               0.45 |      0.59 |  4.78 |       0.02 |
58| PassiveAggressiveRegressor    |               0.37 |      0.54 |  5.09 |       0.01 |
59| GaussianProcessRegressor      |               0.23 |      0.43 |  5.65 |       0.03 |
60| OrthogonalMatchingPursuit     |               0.16 |      0.38 |  5.89 |       0.01 |
61| ExtraTreeRegressor            |               0.08 |      0.32 |  6.17 |       0.01 |
62| DummyRegressor                |              -0.38 |     -0.02 |  7.56 |       0.01 |
63| LassoLars                     |              -0.38 |     -0.02 |  7.56 |       0.01 |
64| KernelRidge                   |             -11.50 |     -8.25 | 22.74 |       0.01 |
65