from sklearn.neighbors import KNeighborsClassifier cancerModel=KNeighborsClassifier(algorithm=‘kd_tree’,n_neighbors=4) cancerModel.fit(X_trainingSet, y_trainingSet) y_predictSet = cancerModel.predict(X_testSet) from sklearn.metrics import accuracy_score print(accuracy_score(y_testSet, y_predictSet))

这段代码中存在一个语法错误。在第二行，`KNeighborsClassifier` 的参数 `algorithm` 和 `n_neighbors` 的赋值中，使用的引号是中文引号，而不是英文引号。请将该行代码中的引号改为英文引号即可解决该错误。修改后的代码如下：

from sklearn.neighbors import KNeighborsClassifier
cancerModel = KNeighborsClassifier(algorithm='kd_tree', n_neighbors=4)
cancerModel.fit(X_trainingSet, y_trainingSet)
y_predictSet = cancerModel.predict(X_testSet)
from sklearn.metrics import accuracy_score
print(accuracy_score(y_testSet, y_predictSet))

相关问题

from sklearn.neighbors import KNeighborsClassifier cancerModel= KNeighborsClassifier(algorithm='kd_tree') cancerModel.fit(X_trainingSet, y_trainingSet) y_predictSet = cancerModel.predict(X_testSet) print(y_predictSet)

这段代码是用来训练一个K近邻分类器（K-Nearest Neighbor Classifier）并进行预测。具体来说，它使用了sklearn库中的KNeighborsClassifier类进行分类器的初始化，并将算法参数设置为kd_tree。接着，使用训练集数据（X_trainingSet和y_trainingSet）对分类器进行训练。随后，使用测试集数据（X_testSet）对训练好的分类器进行测试，并将预测结果存储在y_predictSet中。最后，使用print函数输出预测结果。需要注意的是，这段代码并没有给出X_trainingSet和y_trainingSet的具体定义，因此需要在代码其他位置进行定义。

请教学式按句详细讲解以下代码：###--------------------KNN算法与决策树算法-------------------- from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split # 将文本数据转化为数值特征 vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(data_str_list) # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 特征缩放 scaler = StandardScaler() X_train = scaler.fit_transform(X_train.toarray()) X_test = scaler.transform(X_test.toarray()) from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import GridSearchCV from sklearn.metrics import accuracy_score # 使用网格搜索进行超参数调优 param_grid = { "n_neighbors": [3, 5, 7, 9], "weights": ["uniform", "distance"], "algorithm": ["auto", "ball_tree", "kd_tree", "brute"] } knn = KNeighborsClassifier() grid_search = GridSearchCV(knn, param_grid, cv=5) grid_search.fit(X_train, y_train) print("KNN最优参数：", grid_search.best_params_) param_grid = { "criterion": ["gini", "entropy"], "max_depth": [3, 5, 7, 9] } dt = DecisionTreeClassifier() grid_search = GridSearchCV(dt, param_grid, cv=5) grid_search.fit(X_train, y_train) print("决策树最优参数：", grid_search.best_params_) # 训练分类器并进行预测 knn = KNeighborsClassifier(n_neighbors=5, weights="uniform", algorithm="auto") knn.fit(X_train, y_train) knn_pred = knn.predict(X_test) dt = DecisionTreeClassifier(criterion="gini", max_depth=9) dt.fit(X_train, y_train) dt_pred = dt.predict(X_test) # 混合使用KNN和决策树进行文本分类 ensemble_pred = [] for i in range(len(knn_pred)): if knn_pred[i] == dt_pred[i]: ensemble_pred.append(knn_pred[i]) else: ensemble_pred.append(knn_pred[i]) # 输出分类结果和准确率 print("KNN准确率：", accuracy_score(y_test, knn_pred)) print("决策树准确率：", accuracy_score(y_test, dt_pred)) print("混合使用准确率：", accuracy_score(y_test, ensemble_pred))

这段代码的作用是使用KNN算法和决策树算法对文本进行分类，具体步骤如下：

1.导入所需的库：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score

其中，TfidfVectorizer用于将文本数据转化为数值特征，StandardScaler用于特征缩放，train_test_split用于划分数据集为训练集和测试集，KNeighborsClassifier和DecisionTreeClassifier分别用于KNN算法和决策树算法，GridSearchCV用于超参数调优，accuracy_score用于计算准确率。

2.将文本数据转化为数值特征：

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(data_str_list)

其中，data_str_list为文本数据列表，fit_transform方法将文本数据转化为数值特征X。

3.划分数据集为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

其中，test_size指定测试集占比，random_state指定随机种子，y为文本数据对应的标签。

4.特征缩放：

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train.toarray())
X_test = scaler.transform(X_test.toarray())

使用StandardScaler对特征进行缩放，fit_transform方法用于拟合和转换训练集特征，transform方法用于转换测试集特征。

5.使用网格搜索进行超参数调优：

param_grid = {
    "n_neighbors": [3, 5, 7, 9],
    "weights": ["uniform", "distance"],
    "algorithm": ["auto", "ball_tree", "kd_tree", "brute"]
}
knn = KNeighborsClassifier()
grid_search = GridSearchCV(knn, param_grid, cv=5)
grid_search.fit(X_train, y_train)
print("KNN最优参数：", grid_search.best_params_)

param_grid = {
    "criterion": ["gini", "entropy"],
    "max_depth": [3, 5, 7, 9]
}
dt = DecisionTreeClassifier()
grid_search = GridSearchCV(dt, param_grid, cv=5)
grid_search.fit(X_train, y_train)
print("决策树最优参数：", grid_search.best_params_)

使用GridSearchCV对KNN算法和决策树算法进行超参数调优，其中param_grid指定超参数搜索范围，cv指定交叉验证的次数，best_params_输出最优的超参数组合。

6.训练分类器并进行预测：

knn = KNeighborsClassifier(n_neighbors=5, weights="uniform", algorithm="auto")
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)

dt = DecisionTreeClassifier(criterion="gini", max_depth=9)
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)

使用最优的超参数组合训练分类器，并对测试集进行预测。

7.混合使用KNN和决策树进行文本分类：

ensemble_pred = []
for i in range(len(knn_pred)):
    if knn_pred[i] == dt_pred[i]:
        ensemble_pred.append(knn_pred[i])
    else:
        ensemble_pred.append(knn_pred[i])

将KNN算法和决策树算法的预测结果进行比较，如果两种算法预测结果一致，则将结果添加到ensemble_pred中。

8.输出分类结果和准确率：

print("KNN准确率：", accuracy_score(y_test, knn_pred))
print("决策树准确率：", accuracy_score(y_test, dt_pred))
print("混合使用准确率：", accuracy_score(y_test, ensemble_pred))

使用accuracy_score函数计算分类器的准确率，并输出分类结果。