print('The accuracy of decision tree is', dtc.score(x_test, y_test)) print(classification_report(dtc_y_pred, y_test)) #输出随机森林分类器在测试集上的分类准确性,以及更加详细的精确率、召回率、F1指标。 print('The accuracy of random forest classifier is', rfc.score(x_test, y_test)) pr...
rfc = RandomForestClassifier(random_state = 3, class_weight={0: 1, 1: 5}) 关于结果classification_report 预测出25个正样本,对了11个,共474个真实正样本。准确率0.44, 召回率0.023
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report import matplotlib.pyplot as plt import seaborn as sns import numpy as np from sklearn.metrics import confusion_...
3.4循环遍历每个拆分,并使用随机森林分类器对每个拆分进行训练和评估 from sklearn.metrics import confusion_matrix, classification_report,accuracy_score # 循环遍历每个拆分,并使用随机森林分类器对每个拆分进行训练和评估 for train_index, test_index in ss.split(X, y): X_train, X_test = X[train_index]...
cr = classification_report(y_test, y_pred) # 分类报告 print(f"cr: \n{cr}") 2.8 模型的评价 acc = accuracy_score(y_test, y_pred) # 准确率acc print(f"acc: \n{acc}") cm = confusion_matrix(y_test, y_pred) # 混淆矩阵
(y_test, y_pred)) # 输出分类结果矩阵print("Classification Report:")print(classification_report(y_test, y_pred)) # 输出混淆矩阵print("Accuracy:")print(accuracy_score(y_test, y_pred))print(clf.predict(X_train)) # 此处用作预测,预测数据可以用另一个...
ax.set_title('Confusion Matrix - Random Forest Classification', fontsize=16) plt.show() plot_confusion_matrix(y_test, prediction) print(f"Classification report:\n{classification_report(y_test, prediction)}") print("") print("_"*12) ...
print'The accuracy of gradient tree boosting is', gbc.score(X_test, y_test)printclassification_report(gbc_y_pred, y_test) 单一决策树结果: 随机森林,GDBT结果: 预测性能: GDBT最佳,随机森林次之 一般,工业界为了追求更加强劲的预测性能,使用随机森林作为基线系统(Baseline System)。
print(metrics.classification_report(y_test,predict_target)) print(metrics.confusion_matrix(y_test, predict_target)) print('RF训练集:') predict_Target=rf1.predict(X_train) print(metrics.classification_report(y_train,predict_Target)) print(metrics.confusion_matrix(y_train, predict_Target)) ...
其次对分类器模型进行评估处理,调用accuracy_score()得出预测结果正确的百分比,使用sklearn.metrics.classification_report对分类器进行结果报告分析,了解构建的随机森林情感分类模型的精确率、召回率以及F1-score,所谓精确率,即预测结果为正例样本中真实为正例的比例;召回率,即真实为正例的样本中预测结果为正例的比例;...