ValueError:发现样本数量不一致的输入变量：[6，1]

import numpy as np

# 假设 features 和 labels 是你的特征矩阵和目标向量
features = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
labels = np.array([0, 1, 2, 3, 4, 5])

# 检查样本数量是否一致
if features.shape[0] != labels.shape[0]:
    raise ValueError(f"样本数量不一致: features={features.shape[0]}, labels={labels.shape[0]}")

import pandas as pd

# 假设 data 是你的数据框
data = pd.read_csv('your_data.csv')

# 分离特征和标签
features = data.drop('target', axis=1).values
labels = data['target'].values

# 检查样本数量是否一致
if features.shape[0] != labels.shape[0]:
    raise ValueError(f"样本数量不一致: features={features.shape[0]}, labels={labels.shape[0]}")

from sklearn.model_selection import train_test_split

# 假设 features 和 labels 是你的特征矩阵和目标向量
features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 检查样本数量是否一致
if features_train.shape[0] != labels_train.shape[0] or features_test.shape[0] != labels_test.shape[0]:
    raise ValueError(f"样本数量不一致: train={features_train.shape[0]}, test={features_test.shape[0]}")