如何在Python中进行数据可靠性验证和模型评估

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 检查缺失值
missing_values = data.isnull().sum()
print(missing_values)

import seaborn as sns

# 读取数据
data = pd.read_csv('data.csv')

# 绘制箱线图
sns.boxplot(x='feature', data=data)

import seaborn as sns

# 读取数据
data = pd.read_csv('data.csv')

# 绘制数据分布图
sns.distplot(data['feature'], kde=False)

from sklearn.metrics import accuracy_score

# 真实标签
y_true = [0, 1, 1, 0, 1]

# 预测标签
y_pred = [0, 1, 0, 0, 1]

# 计算准确率
accuracy = accuracy_score(y_true, y_pred)
print(accuracy)

from sklearn.metrics import precision_score, recall_score

# 真实标签
y_true = [0, 1, 1, 0, 1]

# 预测标签
y_pred = [0, 1, 0, 0, 1]

# 计算精确率
precision = precision_score(y_true, y_pred)

# 计算召回率
recall = recall_score(y_true, y_pred)

print(precision, recall)

from sklearn.metrics import f1_score

# 真实标签
y_true = [0, 1, 1, 0, 1]

# 预测标签
y_pred = [0, 1, 0, 0, 1]

# 计算F1分数
f1 = f1_score(y_true, y_pred)
print(f1)