拆分pandas数据帧以提供给scikit-learn SVC Classifier

Question

我有一个看起来像

的pandas数据框

    a           b           c           y
0   0.004539    0.818678    194.381891  0
1   0.001367    0.243724    245.378577  0
2   1.579454    0.465842    849.943583  0
3   7.189778    0.456895    129.707932  0
4   97.743634   0.319419    120.998294  1

我想将a，b，c列分为feature和y分为标签，以便将它们用于scikit-learn SVC分类器。

我做了

features = df[['a', 'b', 'c']]
labels = df['y']

d_train, d_test, l_train, l_test = \
        train_test_split(features, labels,
                         test_size=0.3, random_state=0)

并通过

训练模型

model = SVC()
model.fit(d_train, l_train)

但这给了我一个错误

'SVC' object is not iterable

我没有得到我所缺少的东西。

进口：

import pandas as pd
import numpy as np

import sklearn as sk
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score