如何修复“ValueError：不支持连续类型数据的分类。”
machine-learning 205
原文标题 ：How to fix “ValueError: Classification with data of type continuous is not supported.”
我正在尝试将 auto sklearn 用于一些熊猫数据，当我运行时：
model.fit(X_train, y_train)
弹出此错误：
ValueError Traceback (most recent call last)
<ipython-input-10-ed5cd6b32087> in <module>
2 # sklearn.model_selection.train_test_split(X, y, random_state=1)
3
----> 4 model.fit(X_train, y_train)
~/notebook/jupyterenv/lib/python3.6/site-packages/autosklearn/estimators.py in fit(self, X, y, X_test, y_test, feat_type, dataset_name)
660 "".format(
661 target_type,
--> 662 supported_types
663 )
664 )
ValueError: Classification with data of type continuous is not supported. Supported types are ['binary', 'multiclass', 'multilabel-indicator']. You can find more information about scikit-learn data types in: https://scikit-learn.org/stable/modules/multiclass.html
我的 (X,y) 数据看起来像这样：（标题 HOMO/LUMO 等是描述符）
HOMO (A) HOMO (AH) LUMO (A) LUMO (AH) charge (AH) Charge metal (A) \
0 -7.8453 -9.6920 -4.2406 -6.9161 1 -0.938
1 -7.7330 -9.6774 -4.0690 -6.9602 1 -0.911
2 -7.6751 -9.6051 -3.9238 -6.8990 1 -0.950
3 -8.1345 -9.8027 -6.3221 -7.5155 1 -0.868
4 -7.9405 -9.4709 -5.7324 -6.9515 1 -0.880
.. ... ... ... ... ... ...
164 -7.5867 -9.7576 -5.1992 -6.8152 1 -0.312
165 -8.3700 -10.1670 -6.6819 -7.8044 1 -0.311
166 -8.3445 -10.0288 -6.6499 -7.5991 1 -0.321
167 -7.9764 -10.0586 -6.3554 -7.5688 1 -0.277
168 -7.9317 -9.9008 -6.3104 -7.3790 1 -0.288
[169 rows x 17 columns] [24.4 23.8 24. 14.2 22.5 18.5 19.4 17.4 22.6 16.3 20.3 13.2 16.5 21.2
24.6 17.3 23.3 22.2 18. 31.1 29.7 30.4 22. 23.2 22.1 27.6 22.9 19.8
18.3 18.5 44.8 39.4 46. 49.9 35. 22.5 32. 22.8 38.1 23.6 23.3 18.4
15.6 11.3 13.3 13.9 16.1 20.8 23. 20.4 8.3 11.3 11.4 15.1 15.4 17.1
18.7 21.1 26.6 23. 20.4 21.6 26.8 9. 11.4 32.7 -1.6 -0.3 -1.3 -0.4
-3.9 1. 5.6 0.5 0. 4.5 6.8 7.8 4.2 1.1 4.2 5.5 0.8 12.
17. 5.8 17. 26.1 27.2 31.9 0.5 1.5 8.5 7.1 25.5 40. -5.7 -6.
12.5 4.4 -5. -1.3 -5. -5. -5. -5. -0.6 -0.6 2. 3.6 3.2 0.1
2.1 4.5 11. 2.7 3.5 -2. 1.2 9.3 2.6 7.1 6.1 3.2 5.1 7.5
1.8 4.3 4.4 0.8 9.9 7.6 7.9 8.9 10. 10.9 11.8 9.9 13.4 13.4
8.8 2.1 6. 7.1 -1.1 0.5 0.3 4.7 6. 6.5 8. 11.6 6.9 8.4
8.7 7.2 6.3 6.4 7.4 12.1 10.4 11.1 12.2 14.3 16.3 8.1 8.5 8.6
9. ]
