lazypredict 0.2.7

懒惰的预测

Lazy Predict有助于构建大量的基本模型，而无需太多代码，并且有助于了解哪些模型在不进行任何参数调整的情况下工作得更好

• 免费软件：麻省理工学院许可证
• 文档：https://lazypredict.readthedocs.io。在

使用

要在项目中使用Lazy Predict，请执行以下操作：

import lazypredict

分类

示例

回归

示例

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 |

历史