我正在尝试使用Python中的Sklearn进行k-fold交叉验证，现在已经遵循了两个教程，但我的代码不会运行验证。
每次我尝试
cross_val_score(dt, x, y, cv=5)
我收到错误：
Traceback (most recent call last):
File "C:/Users/djsg38/Documents/CS6001-SpatialTemporal/HW2/main.py", line 573, in <module>
scores = cross_val_score(dt, x, y, cv=5)
File "C:\Python27\lib\site-packages\sklearn\model_selection\_validation.py", line 128, in cross_val_score
X, y, groups = indexable(X, y, groups)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 206, in indexable
check_consistent_length(*result)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 177, in check_consistent_length
lengths = [_num_samples(X) for X in arrays if X is not None]
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 116, in _num_samples
'estimator %s' % x)
TypeError: Expected sequence or array-like, got estimator Its official US President Barack Obama wants lawmakers weigh \
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whether ... Heh heh funny disassociate personWere \
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therehighlightAs indepth umpireshighlightThe headhighlightTwo \
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disrespect
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[100 rows x 12993 columns]
这是我的代码：
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return (df_mod, targets)
df = pd.read_csv("C:/Users/djsg38/Documents/CS6001- SpatialTemporal/HW2/finalCounts.csv")
df2, targets = encode_target(df, "MYLABEL")
features = list(df2.columns[:12338])
y = df2["TARGET"]
x = df2[features]
dt = DecisionTreeClassifier()
dt.fit(x, y)
scores = cross_val_score(dt, x, y, cv=5)
我的DecisionTreeClassifier似乎运行正常，当我将其作为图像输出时看起来不错，但这里的问题在于最后一行。
P.S。我不确定是否有列限制？我遵循的经典示例使用了Iris数据集，因此有四列可以查看数据。但对我来说，我有12,338列数据（100篇文章中每个独特单词的字数）。
答案 0 :(得分：0)
与我所遵循的教程相反，我无法通过我的X值，因为它收到了错误。原因可能是由于其中包含字符串标题，而不是正面。
我所做的解决方案只是手动将我的数据拆分5倍，并对数据执行5个不同的决策树，每次都有1个测试集。