我刚开始学习机器学习的基础知识，就偶然发现了这个错误。
在机器学习中的鸢尾花问题中，我遇到了一个错误，我无法弄清楚为什么我会得到它。你能解释一下为什么我面临这样的错误吗？
法典
from sklearn.datasets import load_iris
from sklearn import tree
import numpy as np
#load data
iris= load_iris()
#position of the start of the flower names or indexes
test_index=[0,50,100]
#Training data
train_target = np.delete(iris.target,test_index)
train_data=np.delete(iris.data,test_index)
test_target=iris.target[test_index]
test_data=iris.data[test_index]
clf = tree.DecisionTreeClassifier()
clf=clf.fit(train_data , train_target)
print(test_target)
错误
Traceback (most recent call last):
File "MachineLearning2.py", line 29, in <module>
clf=clf.fit(train_data , train_target)
File "C:Anacondaslibsite-packagessklearntreetree.py", line 790, in fit
X_idx_sorted=X_idx_sorted)
File "C:Anacondaslibsite-packagessklearntreetree.py", line 116, in fit
X = check_array(X, dtype=DTYPE, accept_sparse="csc")
File "C:Anacondaslibsite-packagessklearnutilsvalidation.py", line 441, in check_array
"if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[ 3.5 1.39999998 0.2 4.9000001 3. 1.39999998
0.2 4.69999981 3.20000005 1.29999995 0.2 4.5999999
3.0999999 1.5 0.2 5. 3.5999999 1.39999998
0.2 5.4000001 3.9000001 1.70000005 0.40000001 4.5999999
3.4000001 1.39999998 0.30000001 5. 3.4000001 1.5 0.2
4.4000001 2.9000001 1.39999998 0.2 4.9000001 3.0999999
1.5 0.1 5.4000001 3.70000005 1.5 0.2
4.80000019 3.4000001 1.60000002 0.2 4.80000019 3. 0.1
4.30000019 3. 1.10000002 0.1 5.80000019 4.
1.20000005 0.2 5.69999981 4.4000001 1.5 0.40000001
5.4000001 3.9000001 1.29999995 0.40000001 5.0999999 3.5
1.39999998 0.30000001 5.69999981 3.79999995 1.70000005 0.30000001
5.0999999 3.79999995 1.5 0.30000001 5.4000001 3.4000001
1.70000005 0.2 5.0999999 3.70000005 1.5 0.40000001
4.5999999 3.5999999 1. 0.2 5.0999999 3.29999995
1.70000005 0.5 4.80000019 3.4000001 1.89999998 0.2 3.
1.60000002 0.2 5. 3.4000001 1.60000002 0.40000001
5.19999981 3.5 1.5 0.2 5.19999981 3.4000001
1.39999998 0.2 4.69999981 3.20000005 1.60000002 0.2
4.80000019 3.0999999 1.60000002 0.2 5.4000001 3.4000001
1.5 0.40000001 5.19999981 4.0999999 1.5 0.1 5.5
4.19999981 1.39999998 0.2 4.9000001 3.0999999 1.5 0.1
5. 3.20000005 1.20000005 0.2 5.5 3.5
1.29999995 0.2 4.9000001 3.0999999 1.5 0.1
4.4000001 3. 1.29999995 0.2 5.0999999 3.4000001
1.5 0.2 5. 3.5 1.29999995 0.30000001
4.5 2.29999995 1.29999995 0.30000001 4.4000001 3.20000005
1.29999995 0.2 5. 3.5 1.60000002 0.60000002
5.0999999 3.79999995 1.89999998 0.40000001 4.80000019 3.
1.39999998 0.30000001 5.0999999 3.79999995 1.60000002 0.2
4.5999999 3.20000005 1.39999998 0.2 5.30000019 3.70000005
1.5 0.2 5. 3.29999995 1.39999998 0.2 7.
3.20000005 4.69999981 1.39999998 6.4000001 3.20000005 4.5 1.5
6.9000001 3.0999999 4.9000001 1.5 5.5 2.29999995
4. 1.29999995 6.5 2.79999995 4.5999999 1.5
5.69999981 2.79999995 4.5 1.29999995 6.30000019 3.29999995
4.69999981 1.60000002 4.9000001 2.4000001 3.29999995 1. 6.5999999
2.9000001 4.5999999 1.29999995 5.19999981 2.70000005 3.9000001
1.39999998 5. 2. 3.5 1. 5.9000001 3.
4.19999981 1.5 6. 2.20000005 4. 1. 6.0999999
2.9000001 4.69999981 1.39999998 5.5999999 2.9000001 3.5999999
1.29999995 6.69999981 3.0999999 4.4000001 1.39999998 5.5999999 3.
4.5 1.5 5.80000019 2.70000005 4.0999999 1.
6.19999981 2.20000005 4.5 1.5 5.5999999 2.5
3.9000001 1.10000002 5.9000001 3.20000005 4.80000019 1.79999995
6.0999999 2.79999995 4. 1.29999995 6.30000019 2.5
4.9000001 1.5 6.0999999 2.79999995 4.69999981 1.20000005
6.4000001 2.9000001 4.30000019 1.29999995 6.5999999 3. 4.4000001
1.39999998 6.80000019 2.79999995 4.80000019 1.39999998 6.69999981
3. 5. 1.70000005 6. 2.9000001 4.5 1.5
5.69999981 2.5999999 3.5 1. 5.5 2.4000001
3.79999995 1.10000002 5.5 2.4000001 3.70000005 1.
5.80000019 2.70000005 3.9000001 1.20000005 6. 2.70000005
5.0999999 1.60000002 5.4000001 3. 4.5 1.5 6.
3.4000001 4.5 1.60000002 6.69999981 3.0999999 4.69999981
1.5 6.30000019 2.29999995 4.4000001 1.29999995 5.5999999 3.
4.0999999 1.29999995 5.5 2.5 4. 1.29999995
5.5 2.5999999 4.4000001 1.20000005 6.0999999 3. 4.5999999
1.39999998 5.80000019 2.5999999 4. 1.20000005 5.
2.29999995 3.29999995 1. 5.5999999 2.70000005 4.19999981
1.29999995 5.69999981 3. 4.19999981 1.20000005 5.69999981
2.9000001 4.19999981 1.29999995 6.19999981 2.9000001 4.30000019
1.29999995 5.0999999 2.5 3. 1.10000002 5.69999981
2.79999995 4.0999999 1.29999995 6.30000019 3.29999995 6. 2.5
5.80000019 2.70000005 5.0999999 1.89999998 7.0999999 3. 5.9000001
2.0999999 6.30000019 2.9000001 5.5999999 1.79999995 6.5 3.
5.80000019 2.20000005 7.5999999 3. 6.5999999 2.0999999
4.9000001 2.5 4.5 1.70000005 7.30000019 2.9000001
6.30000019 1.79999995 6.69999981 2.5 5.80000019 1.79999995
7.19999981 3.5999999 6.0999999 2.5 6.5 3.20000005
5.0999999 2. 6.4000001 2.70000005 5.30000019 1.89999998
6.80000019 3. 5.5 2.0999999 5.69999981 2.5 5.
2. 5.80000019 2.79999995 5.0999999 2.4000001 6.4000001
3.20000005 5.30000019 2.29999995 6.5 3. 5.5
1.79999995 7.69999981 3.79999995 6.69999981 2.20000005 7.69999981
2.5999999 6.9000001 2.29999995 6. 2.20000005 5. 1.5
6.9000001 3.20000005 5.69999981 2.29999995 5.5999999 2.79999995
4.9000001 2. 7.69999981 2.79999995 6.69999981 2.
6.30000019 2.70000005 4.9000001 1.79999995 6.69999981 3.29999995
5.69999981 2.0999999 7.19999981 3.20000005 6. 1.79999995
6.19999981 2.79999995 4.80000019 1.79999995 6.0999999 3. 4.9000001
1.79999995 6.4000001 2.79999995 5.5999999 2.0999999 7.19999981
3. 5.80000019 1.60000002 7.4000001 2.79999995 6.0999999
1.89999998 7.9000001 3.79999995 6.4000001 2. 6.4000001
2.79999995 5.5999999 2.20000005 6.30000019 2.79999995 5.0999999
1.5 6.0999999 2.5999999 5.5999999 1.39999998 7.69999981
3. 6.0999999 2.29999995 6.30000019 3.4000001 5.5999999
2.4000001 6.4000001 3.0999999 5.5 1.79999995 6. 3.
4.80000019 1.79999995 6.9000001 3.0999999 5.4000001 2.0999999
6.69999981 3.0999999 5.5999999 2.4000001 6.9000001 3.0999999
5.0999999 2.29999995 5.80000019 2.70000005 5.0999999 1.89999998
6.80000019 3.20000005 5.9000001 2.29999995 6.69999981 3.29999995
5.69999981 2.5 6.69999981 3. 5.19999981 2.29999995
6.30000019 2.5 5. 1.89999998 6.5 3.
5.19999981 2. 6.19999981 3.4000001 5.4000001 2.29999995
5.9000001 3. 5.0999999 1.79999995].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
>>>
您还能解释为什么会发生这种阵列错误的重塑吗？
提前谢谢。
更改以下行：
train_data=np.delete(iris.data,test_index)
自：
train_data=np.delete(iris.data,test_index, axis=0)
你会很高兴去。根据numpy.delete的文档：
轴 ： 整数， 可选
The axis along which to delete the subarray defined by obj. If axis is None, obj is applied to the flattened array.
由于您没有提供要从行或列中删除索引，因此 numpy 正在展平数组，这将是错误的。
通过使用 axis=0，我们指定要删除行。
错误消息是这样的
ValueError: Expected 2D array, got 1D array instead
以下似乎是库生成的建议。按照它告诉你的去做尝试。 reshape实际上是一种非常有用的技术，用于规范机器学习的输入大小。跟踪您输入的用于训练和测试的形状将进一步是一种很好的做法。
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.