我对 SHAP 很陌生，我想尝试一下，但遇到了一些困难。
该模型已经过训练并且似乎表现良好。然后我使用训练数据来测试 SHAP。看起来像这样:
var_Braeburn var_Cripps Pink var_Dazzle var_Fuji var_Granny Smith \
0 1 0 0 0 0
1 0 1 0 0 0
2 0 1 0 0 0
3 0 1 0 0 0
4 0 1 0 0 0
var_Other Variety var_Royal Gala (Tenroy) root_CG202 root_M793 \
0 0 0 0 0
1 0 0 1 0
2 0 0 1 0
3 0 0 0 0
4 0 0 0 0
root_MM106 ... frt_BioRich Organic Compost_single \
0 1 ... 0
1 0 ... 0
2 0 ... 0
3 1 ... 0
4 1 ... 0
frt_Biomin Boron_single frt_Biomin Zinc_single \
0 0 1
1 0 0
2 0 0
3 0 0
4 0 0
frt_Fertco Brimstone90 sulphur_single frt_Fertco Guano _single \
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
frt_Gro Mn_multiple frt_Gro Mn_single frt_Organic Mag Super_multiple \
0 0 0 0
1 1 0 1
2 1 0 1
3 1 0 1
4 1 0 1
frt_Organic Mag Super_single frt_Other Fertiliser
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
然后我执行explainer = shap.Explainer(model)和shap_values =explainer(X_train)
运行没有错误，shap_values 给了我这个:
.values =
array([[[ 0.00775555, -0.00775555],
[-0.03221035, 0.03221035],
[-0.0027203 , 0.0027203 ],
...,
[ 0.00259787, -0.00259787],
[-0.00459262, 0.00459262],
[-0.0303394 , 0.0303394 ]],
[[-0.00068313, 0.00068313],
[-0.03006355, 0.03006355],
[-0.00245706, 0.00245706],
...,
[-0.00418809, 0.00418809],
[-0.00088372, 0.00088372],
[-0.00030019, 0.00030019]],
[[-0.00068313, 0.00068313],
[-0.03006355, 0.03006355],
[-0.00245706, 0.00245706],
...,
[-0.00418809, 0.00418809],
[-0.00088372, 0.00088372],
[-0.00030019, 0.00030019]],
...,
但是，当我运行 shap.plots.beeswarm(shap_values) 时，出现以下错误:
ValueError:蜂群图不支持对具有多个维度的实例进行绘图解释!
我在这里做错了什么？
最佳答案
试试这个:
from shap import Explainer
from shap.plots import beeswarm
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True, as_frame = True)
model = RandomForestClassifier().fit(X, y)
explainer = Explainer(model)
sv = explainer(X)
然后，由于 RF 有点特殊，因此仅检索第 1 类的 shap 值:
beeswarm(sv[:,:,1])
关于python - SHAP - 具有多个维度的实例，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/76083485/