score_train = accuracy_score(y_train,y_train_pred) print("训练集Classification Report:\n ", classification_report(y_train,y_train_pred)) score_test = accuracy_score(y_test,y_test_pred) print("测试集Classification Report:\n ", classification_report(y_test,y_test_pred)) print('训练集Accu...
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
cm = confusion_matrix(y_test, y_pred) #混淆矩阵print(f"cm: \n{cm}") 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_...
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...
首先，我们将导入必要的库：import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report 接下来，我们将加载数据集...
print(classification_report(y_test, y_pred)) precision recall f1-score support 0 0.62 0.42 0.51 59 1 0.79 0.89 0.84 141 accuracy 0.76 200 macro avg 0.71 0.66 0.67 200 weighted avg 0.74 0.76 0.74 200 In [32]: #得到变量重要性排名importance =classifier.feature_importances_importmatplotlib.pyplo...
(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...
# 打印分类报告print('预测数据的分类报告为:','\n',classification_report(y_test,y_pred))# 这行代码在jupyter notebook 上面运行不起,内存不足,需要使用本地的pycharm,好像我的也跑不起# score_pre = cross_val_score(model,X_test,y_test,cv=5).mean() #利用所有数据,进行交叉验证以后0.976变差了...
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(classification_report(y_test, y_pred)) print(cross_val_score(clf, data.data, data.target, cv=3))[0.98 0.94 0.96] 这个例子说明了 LCE 对缺失值的鲁棒性。 使用每个变量 20% 的缺失值对 Iris 训练集进行了修改。 import numpy as npfrom lce import LCEClassifierfrom sklearn.datasets import...