Python scikit学习测试数据分割：ValueError:找到样本数不一致的输入变量：[49995000]
这是我的密码Python scikit学习测试数据分割：ValueError:找到样本数不一致的输入变量：[49995000],python,scikit-learn,train-test-split,Python,Scikit Learn,Train Test Split,这是我的密码 print(len(image_dataset.data)) print(len(phylum_target)) X_train, X_test, y_train, y_test = train_test_split(image_dataset.data, phylum_target, test_size=0.2,random_state=109) 这是输出和错误 5000 5000 Traceback (most recent call last): File "
print(len(image_dataset.data))
print(len(phylum_target))
X_train, X_test, y_train, y_test = train_test_split(image_dataset.data, phylum_target, test_size=0.2,random_state=109)
5000
5000
Traceback (most recent call last):
File "Image_SVM_run_only.py", line 298, in <module>
X_train_temp, X_test_temp, y_train_temp, y_test_temp = train_test_split(image_dataset.data, phylum_target, test_size=0.2,random_state=109)
File "/root/anaconda3/envs/IBC/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 2127, in train_test_split
arrays = indexable(*arrays)
File "/root/anaconda3/envs/IBC/lib/python3.7/site-packages/sklearn/utils/validation.py", line 293, in indexable
check_consistent_length(*result)
File "/root/anaconda3/envs/IBC/lib/python3.7/site-packages/sklearn/utils/validation.py", line 257, in check_consistent_length
" samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [4999, 5000]
5000
5000
回溯（最近一次呼叫最后一次）：
文件“Image\u SVM\u run\u only.py”，第298行，在
X_-train_temp，X_-test_temp，y_-train_temp，y_-test_temp=train_-test_分割（image_-dataset.data，门目标，测试大小=0.2，随机状态=109）
文件“/root/anaconda3/envs/IBC/lib/python3.7/site packages/sklearn/model\u selection/\u split.py”，第2127行，列车内测试\u split
数组=可索引（*数组）
文件“/root/anaconda3/envs/IBC/lib/python3.7/site packages/sklearn/utils/validation.py”，第293行，可索引
检查长度是否一致（*结果）
文件“/root/anaconda3/envs/IBC/lib/python3.7/site packages/sklearn/utils/validation.py”，第257行，检查长度是否一致
“样本：%r”%[int（l）表示长度为l的样本]）
ValueError:找到样本数不一致的输入变量：[49995000]
请帮助我T.T这是我能从你的信息中辨别出的最小可重复性示例，效果很好
import numpy as np
from sklearn.model_selection import train_test_split
X = np.zeros((5000, 49152))
y = np.zeros((5000, 1))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=109)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
这是我能从你的信息中辨别出的最小可重复性示例，效果很好
import numpy as np
from sklearn.model_selection import train_test_split
X = np.zeros((5000, 49152))
y = np.zeros((5000, 1))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=109)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
不仅仅是长度，每个的长度/形状[0]是多少？`print（type（image\u dataset.data））print（type（phylum\u target））print（image\u dataset.data.shape）print（phylum\u target.shape）`
（500049152）（5000，1）（500049152）（5000，1）