41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
# 导入所需库
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, classification_report
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import matplotlib.pyplot as plt
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# 1. 加载数据集（这里使用鸢尾花数据集）
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iris = load_iris()
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X = iris.data
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y = iris.target
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# 2. 划分训练集和测试集（80%训练，20%测试）
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 3. 创建随机森林分类器
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rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
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# 4. 训练模型
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rf_classifier.fit(X_train, y_train)
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# 5. 在测试集上进行预测
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y_pred = rf_classifier.predict(X_test)
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# 6. 评估模型性能
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accuracy = accuracy_score(y_test, y_pred)
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print(f"模型准确率：{accuracy:.4f}")
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# 输出详细的分类报告（精确率、召回率、F1分数等）
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print("分类报告：")
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print(classification_report(y_test, y_pred))
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# 7. 可视化特征重要性（可选）
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feature_importances = rf_classifier.feature_importances_
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plt.figure(figsize=(10, 6))
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plt.barh(iris.feature_names, feature_importances)
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plt.xlabel("feature importance")
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plt.title("random forest model feature importance")
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plt.savefig('./output/random_forest_c.png')
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