机器学习

import os
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# 模拟数据
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练基线模型
model = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=42)
model.fit(X_train, y_train)

# 评估
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

使用 scikit-learn 构建监督学习基线并输出评估指标

{
  "precision": 0.89,
  "recall": 0.87,
  "f1_score": 0.88,
  "accuracy": 0.88,
  "class_distribution": {
    "0": 0.52,
    "1": 0.48
  },
  "sample_predictions": [
    {
      "input_id": "sample_001",
      "true_label": 1,
      "predicted_label": 1,
      "confidence": 0.94
    }
  ]
}