机器学习模型性能下降原因分析

机器学习模型性能下降原因分析

现象描述

在生产环境中，某推荐系统模型准确率从0.85下降至0.62，需快速定位问题根源。

监控指标追踪

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# 1. 模型性能指标监控
performance_metrics = {
    'accuracy': [0.85, 0.84, 0.83, 0.82, 0.62],
    'precision': [0.82, 0.81, 0.79, 0.75, 0.58],
    'recall': [0.88, 0.87, 0.85, 0.80, 0.45],
    'f1_score': [0.84, 0.83, 0.81, 0.76, 0.52]
}

# 2. 数据分布监控
data_drift = {
    'feature_1_mean': [0.5, 0.52, 0.55, 0.62, 0.75],
    'feature_2_std': [0.1, 0.12, 0.15, 0.25, 0.35]
}

# 3. 系统资源监控
system_metrics = {
    'cpu_utilization': [65, 70, 75, 85, 95],
    'memory_usage': [45, 50, 55, 65, 75]
}

告警配置方案

# alert_rules.yaml
rules:
  - name: "model_performance_drop"
    condition: "accuracy < 0.70"
    severity: "critical"
    notification_channels: ["slack", "email"]
    duration: "5m"
    
  - name: "data_drift_detected"
    condition: "feature_1_mean > 0.70 OR feature_2_std > 0.30"
    severity: "warning"
    notification_channels: ["slack"]
    duration: "1h"
    
  - name: "system_resource_high"
    condition: "cpu_utilization > 90 OR memory_usage > 80"
    severity: "critical"
    notification_channels: ["pagerduty"]
    duration: "10m"

复现步骤

1. 使用Prometheus抓取指标数据
2. 配置Grafana仪表盘监控关键指标
3. 执行以下诊断脚本定位问题：

# 检查数据分布变化
python data_drift_analysis.py --model_version v1.0 --baseline_date 2023-10-01

# 检查系统负载
top -p $(pgrep python)

# 检查模型预测日志
tail -f /var/log/model_predictions.log | grep "accuracy < 0.7"