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))
时间: 2024-02-22 19:58:39 浏览: 36
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。
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