50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.metrics import (
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accuracy_score, f1_score, roc_auc_score,
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precision_recall_curve, classification_report
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)
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from sklearn.model_selection import train_test_split
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if __name__ == '__main__':
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data = pd.read_csv('tr_user_tj.csv', header=0)
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data1 = data[['star_num', 'sign_num', 'coll_num', 'dna_num', 'task_num', 'word_num', 'balance_amt', 'earn_amt',
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'season_point', 'point', 'star_score', 'term_amt', 'match_num']]
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# 分离特征和目标变量
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X = data1.drop('match_num', axis=1)
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y = data1['match_num']
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# 划分训练集和测试集
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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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# 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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# 训练模型
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rf_classifier.fit(X_train, y_train)
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# 预测
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y_pred = rf_classifier.predict(X_test)
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y_pred_proba = rf_classifier.predict_proba(X_test)[:, 1] if len(np.unique(y)) == 2 else rf_classifier.predict_proba(
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X_test)
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# 计算评估指标
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accuracy = accuracy_score(y_test, y_pred)
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f1 = f1_score(y_test, y_pred, average='weighted')
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print(f"Accuracy: {accuracy}")
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print(f"F1 Score: {f1}")
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print("\nClassification Report:")
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print(classification_report(y_test, y_pred))
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# 特征重要性
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feature_importance = pd.DataFrame({
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'feature': X.columns,
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'importance': rf_classifier.feature_importances_
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}).sort_values('importance', ascending=False)
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print("\nFeature Importance:")
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print(feature_importance)
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