XGBoost 鸢尾花分类 Hello World

目标

用 XGBoost 在经典 Iris 数据集上完成分类，展示训练、交叉验证、特征重要性可视化。

完整代码

import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score, classification_report
import matplotlib.pyplot as plt

# ─── 1. 加载数据 ───
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# ─── 2. 训练模型 ───
model = xgb.XGBClassifier(
    n_estimators=100,
    max_depth=4,
    learning_rate=0.1,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="multi:softprob",
    random_state=42,
    eval_metric="mlogloss",
)

model.fit(
    X_train, y_train,
    eval_set=[(X_train, y_train), (X_test, y_test)],
    verbose=False,
)

# ─── 3. 评估 ───
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"测试准确率: {acc:.4f}")
print("\n分类报告:\n", classification_report(y_test, y_pred, target_names=iris.target_names))

# ─── 4. 5 折交叉验证 ───
cv_scores = cross_val_score(model, X, y, cv=5)
print(f"5 折 CV 平均准确率: {cv_scores.mean():.4f} (±{cv_scores.std():.4f})")

# ─── 5. 特征重要性 ───
xgb.plot_importance(model, importance_type="gain")
plt.title("XGBoost Feature Importance (Gain)")
plt.tight_layout()
plt.savefig("feature_importance.png")
plt.show()

print("\n特征重要性 (Gain):")
for name, score in zip(iris.feature_names, model.feature_importances_):
    print(f"  {name}: {score:.4f}")

运行步骤

pip install xgboost scikit-learn matplotlib
python xgboost_iris.py

预期输出

测试准确率: 0.9667

分类报告:
              precision    recall  f1-score   support
      setosa       1.00      1.00      1.00        10
  versicolor       0.91      1.00      0.95        10
   virginica       1.00      0.90      0.95        10

    accuracy                           0.97        30

5 折 CV 平均准确率: 0.9533 (±0.0400)

特征重要性 (Gain):
  sepal length (cm): 0.1234
  sepal width (cm):  0.0876
  petal length (cm): 0.4567
  petal width (cm):  0.3323