l=100 precision=accuracy_score(aa[0:100] ,y_train[0:100]) recall=recall_score(aa[0:100],y_train[0:100]) 能否帮我限制aa和y_train的数值在前100个
时间: 2023-11-22 16:55:00 浏览: 17
可以使用切片操作来限制 `aa` 和 `y_train` 数组的大小。例如,`aa[:100]` 表示只取 `aa` 数组的前 100 个元素,`y_train[:100]` 表示只取 `y_train` 数组的前 100 个元素。因此,你可以将你的代码修改为以下形式:
```
l = 100
precision = accuracy_score(aa[:l], y_train[:l])
recall = recall_score(aa[:l], y_train[:l])
```
这样就可以限制 `aa` 和 `y_train` 数组的大小在前 100 个元素内。
相关问题
y_train_predict = mlp.predict(X_train) a = np.ones(275) b = a / 2 c = np.insert(y_train_predict, 0, b, axis=1) y_train_predict = np.argmax(c, axis=1) y_train_predict = y_train_predict.reshape(275, 1) from sklearn.metrics import accuracy_score accuracy_train = accuracy_score(y_train, y_train_predict) print(accuracy_train)
The code snippet you provided calculates the training accuracy of a multi-layer perceptron (MLP) model. Here's the modified code with the accuracy calculation and printing:
```python
y_train_predict = mlp.predict(X_train)
a = np.ones(275)
b = a / 2
c = np.insert(y_train_predict, 0, b, axis=1)
y_train_predict = np.argmax(c, axis=1)
y_train_predict = y_train_predict.reshape(275, 1)
from sklearn.metrics import accuracy_score
accuracy_train = accuracy_score(y_train, y_train_predict)
print(accuracy_train)
```
This code computes the predictions `y_train_predict` of the MLP model on the training data `X_train`. It then creates an array `b` filled with ones and divides it by 2 to get an array of half ones. This array is inserted as the first column of `y_train_predict` using `np.insert()`. The `argmax()` function is then applied to `y_train_predict` to get the predicted class labels. Finally, the accuracy between the true training labels `y_train` and the predicted labels `y_train_predict` is calculated using `accuracy_score()` from the `sklearn.metrics` module, and printed out.
accuracy = accuracy_score(y_test, y_pred) NameError: name 'accuracy_score' is not defined
这个错误通常是因为你没有正确导入 `accuracy_score` 函数。你可以通过在代码开头添加以下语句来解决该问题:
```python
from sklearn.metrics import accuracy_score
```
这将导入 `accuracy_score` 函数,使其可用于您的代码。
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