第一种方式:accuracy_score 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 准确率importnumpyasnp from sklearn.metricsimportaccuracy_score y_pred=[0,2,1,3,9,9,8,5,8]y_true=[0,1,2,3,2,6,3,5,9]accuracy_score(y_true,y_pred)Out[127]:0.33333333333333331accuracy_score(y_true,y_...
# Python示例fromsklearn.metricsimportaccuracy_score# 假设预测值和真实值y_true=[0,1,1,0]y_pred=[0,1,0,0]# 加入数据完整性检查iflen(y_true)==len(y_pred):accuracy=accuracy_score(y_true,y_pred)print(f"Accuracy:{accuracy}")else:print("Error: Input lengths do not match.") 1. 2. 3...
fromsklearn.metricsimportaccuracy_score y_true=[1,0,1,1]y_pred=[0,0,1,0]# 错误的预测结果accuracy=accuracy_score(y_true,y_pred)# 计算准确性 1. 2. 3. 4. 5. 在上述代码中,y_true和y_pred的格式需要完全一致,否则将导致accuracy_score计算准确性时发生异常。 根因分析 造成以上错误的根本原...
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X = iris.data y = iris.target # 因为逻辑回归是用于二分类问题,我们这里只取两个类别的数据 # 选择类别为0和1的数据 X = X[y != 2] y = y[y != 2] # 划...
1、准确率 (Accuracy) A = (TP + TN)/(P+N) = (TP + TN)/(TP + FN + FP + TN); 反映了分类器统对整个样本的判定能力——能将正的判定为正,负的判定为负。 2、精确度(Precision) P = TP/(TP+FP); 分对的样本数除以所有的样本数 ,即:准确(分类)率 = 正确预测的正反例数 / ...
acc = accuracy_score(y_test, rf.predict(np.delete(X_test, i, axis=1))) importances.append(base_acc - acc) # Plot importance scores plt.bar(range(len(importances)), importances) plt.show() 4、相关性分析 计算各特征与...
from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X = iris.data y = iris.target # 因为逻辑回归是用于二分类问题,我们这里只取两个类别的数据 # 选择类别为0和1的数据 X = X[y != 2] y = y[y != 2]
fromsklearn.metricsimportaccuracy_score svm_model = SVC() svm_model.fit(train_x,train_y) pred1 = svm_model.predict(train_x) accuracy1 = accuracy_score(train_y,pred1) print('在训练集上的精确度: %.4f'%accuracy1) pred2 = svm_model.predict(te...
对于分类任务，score通常是计算模型在测试集上的准确率（accuracy），即正确预测的样本数与总样本数的比例...
base_acc = accuracy_score(y_test, rf.predict(X_test)) # Initialize empty list to store importances importances = [] # Iterate over all columns and remove one at a time foriinrange(X_train.shape[1]): X_temp = np.delete(X_train, i, axis=1) ...