true_labels = y[unlabeled_indices]什么意思
时间: 2023-06-10 18:07:20 浏览: 40
这行代码假设有一个名为`y`的标签数组和一个名为`unlabeled_indices`的未标记样本的索引数组。该代码行的目的是从`y`数组中提取未标记样本的标签,并将其存储在名为`true_labels`的新数组中。换句话说,它会根据未标记样本的索引,提取对应标签,并将这些标签存储在一个新的数组中,以供后续的操作使用。
相关问题
% Data preparation data = readtable('Attrition_train.csv'); n = size(data, 1); train_ratio = 0.8; train_size = floor(n * train_ratio); train_indices = randsample(n, train_size); test_indices = setdiff(1:n, train_indices); train_data = data(train_indices, :); train_labels = data(train_indices, end); test_data = data(test_indices, :); test_labels = data(test_indices, end); % Model training k = 5; model = knnsearch(train_data(:, 1:end-1), train_labels, 'NumNeighbors', k); % Model test predictions = predict(model, test_data(:, 1:end-1)); accuracy = sum(predictions == test_labels) / length(test_labels); disp(['Accuracy: ', num2str(accuracy)]);报错:参数名称 NumNeighbors 无效。代码更改
根据MATLAB官方文档,`knnsearch`函数的第二个参数应该是查询点的坐标,而不是标签。所以,你需要将第二个参数从 `train_labels` 改为 `train_data(:, 1:end-1)`。同时,在 `knnsearch` 函数中,`NumNeighbors` 参数应该放在函数名之后,而不是放在参数列表中。最终的修改后代码如下所示:
```
% Data preparation
data = readtable('Attrition_train.csv');
n = size(data, 1);
train_ratio = 0.8;
train_size = floor(n * train_ratio);
train_indices = randsample(n, train_size);
test_indices = setdiff(1:n, train_indices);
train_data = data(train_indices, :);
train_labels = train_data.Attrition;
train_data = train_data(:, 1:end-1);
test_data = data(test_indices, :);
test_labels = test_data.Attrition;
test_data = test_data(:, 1:end-1);
% Model training
k = 5;
model = knnsearch(train_data, train_data, 'K', k);
% Model test
predictions = predict(model, test_data);
accuracy = sum(predictions == test_labels) / length(test_labels);
disp(['Accuracy: ', num2str(accuracy)]);
```
注意,我还对数据的读取和预处理部分进行了一些修改,以确保程序能够正确运行。
请帮我检查这段代码的错误:from sklearn.metrics import classification_report, confusion_matrix , ConfusionMatrixDisplay def score_confuse(model_name,true_labels,pre_labels): #输出得分报告 print("\n"+model_name+"的得分报告:") print(classification_report(y_true=true_labels,y_pred=pre_labels) #输出混淆矩阵 disp = ConfusionMatrixDisplay(confusion_matrix(true_labels,pre_labels), display_labels=['0','1']) disp.plot(include_values = True,cmap = plt.cm.Blues,ax=None,xticks_rotation='horizontal',values_format='d') plt.show()
您的代码中存在一个括号未闭合的错误。在 `print(classification_report(y_true=true_labels,y_pred=pre_labels)` 这一行,缺少一个右括号。正确的代码应该是:
```
from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay
def score_confuse(model_name, true_labels, pre_labels):
# 输出得分报告
print("\n" + model_name + "的得分报告:")
print(classification_report(y_true=true_labels, y_pred=pre_labels))
# 输出混淆矩阵
disp = ConfusionMatrixDisplay(confusion_matrix(true_labels, pre_labels), display_labels=['0', '1'])
disp.plot(include_values=True, cmap=plt.cm.Blues, ax=None, xticks_rotation='horizontal', values_format='d')
plt.show()
```
另外,记得导入 `matplotlib.pyplot` 模块,以便您可以成功地显示混淆矩阵。