线性拟合读取100行100列的数据使用交叉验证的代码，error_score='raise'来调试错误，并将数据分成训练数据，测试数据，预测数据并生成拟合图，以及准确率，召回率，F1值且x,y的值相同

以下是线性拟合读取100行100列数据并使用交叉验证进行训练和测试的代码：

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import matplotlib.pyplot as plt

# 读取数据
data = pd.read_csv('data.csv', header=None)
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 将数据分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 线性拟合
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# 交叉验证
scores = cross_val_score(regressor, X_train, y_train, cv=5, scoring='neg_mean_squared_error', error_score='raise')
print("交叉验证得分：", scores)

# 预测数据
y_pred = regressor.predict(X_test)

# 计算准确率、召回率、F1值
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print("准确率：", accuracy)
print("召回率：", recall)
print("F1值：", f1)

# 生成拟合图
plt.scatter(X_test, y_test, color='red')
plt.plot(X_test, y_pred, color='blue')
plt.title('Linear Regression')
plt.xlabel('X')
plt.ylabel('y')
plt.show()

需要注意的是，这里的数据文件名为"data.csv"，需要根据实际情况进行修改。同时，X和y的值应当相同，即数据中最后一列为y值，前面的所有列为X值。