在digits数据集上创建一个knn算法分类器，并计算它的准确度、

好的，我明白了。针对您的问题，我将提供以下步骤：

1. 导入digits数据集和sklearn库中的KNeighborsClassifier模块。

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

2. 加载digits数据集并进行拆分。

digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=0)

3. 创建KNN分类器并进行训练。

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

4. 对测试数据进行预测并计算准确度。

accuracy = knn.score(X_test, y_test)
print("Accuracy:", accuracy)

完整代码如下：

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=0)

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

accuracy = knn.score(X_test, y_test)
print("Accuracy:", accuracy)

这段代码将创建一个KNN分类器并在digits数据集上进行训练和测试，然后输出分类器的准确度。