解释这段代码:# 决策树 dt = DecisionTreeClassifier(max_depth=5, random_state=0) dt.fit(X_train, y_train) y_pred_dt = dt.predict(X_test) print('决策树准确率:', metrics.accuracy_score(y_test, y_pred_dt)) # 决策树可视化 dot_data = export_graphviz(dt, out_file=None, feature_names=X_train.columns, class_names=['Dead', 'Survived'], filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.render('titanic_decision_tree') # 剪枝 dt_pruned = DecisionTreeClassifier(max_depth=5, ccp_alpha=0.01, random_state=0) dt_pruned.fit(X_train, y_train) y_pred_pruned = dt_pruned.predict(X_test) print('剪枝决策树准确率:', metrics.accuracy_score(y_test, y_pred_pruned)) # 随机森林 rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0) rf.fit(X_train, y_train) y_pred_rf = rf.predict(X_test) print('随机森林准确率:', metrics.accuracy_score(y_test, y_pred_rf))
时间: 2023-12-24 07:15:40 浏览: 33
这段代码是用于构建和比较不同决策树和随机森林模型的分类准确率。首先,使用DecisionTreeClassifier函数构建一个决策树模型,设置最大深度为5,随机种子为0,并使用X_train和y_train训练模型,使用X_test预测结果并计算准确率。然后,使用export_graphviz函数将决策树可视化,设置特征名称为X_train的列名,类别名称为Dead和Survived,并将结果图形保存为titanic_decision_tree。接着,使用DecisionTreeClassifier函数构建一个剪枝决策树模型,除了最大深度为5外,还设置了ccp_alpha参数为0.01,并使用X_train和y_train训练模型,使用X_test预测结果并计算准确率。最后,使用RandomForestClassifier函数构建一个随机森林模型,设置树的数量为100,最大深度为5,随机种子为0,并使用X_train和y_train训练模型,使用X_test预测结果并计算准确率。
相关问题
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。
import pandas as pd from sklearn.model_selection import train_test_split # 导入数据并添加列名 columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'Class_Values'] car_data = pd.read_csv('car.data', header=None, names=columns) # 将 Class Values 转换为数字 class_map = {'unacc': 0, 'acc': 1, 'good': 2, 'vgood': 3} car_data['Class_Values'] = car_data['Class_Values'].map(class_map) # 划分训练集和测试集 train_data, test_data = train_test_split(car_data, test_size=0.2, random_state=42) from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 定义特征变量和目标变量 feature_cols = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'] target_col = 'Class_Values' # 建立决策树模型 dt = DecisionTreeClassifier(max_depth=5) dt.fit(train_data[feature_cols], train_data[target_col]) # 对测试数据进行预测 y_pred = dt.predict(test_data[feature_cols]) # 计算评估指标 accuracy = accuracy_score(test_data[target_col], y_pred) precision = precision_score(test_data[target_col], y_pred, average='macro') recall = recall_score(test_data[target_col], y_pred, average='macro') f1 = f1_score(test_data[target_col], y_pred, average='macro') print('Accuracy:', accuracy) print('Precision:', precision) print('Recall:', recall) print('F1 Score:', f1)ValueError: could not convert string to float: 'vhigh'
这是一个值错误(ValueError)的异常,原因是在决策树模型中,特征变量包含字符串类型的数据,无法进行计算,需要将字符串类型的数据转换为数字类型的数据。可以使用sklearn.preprocessing中的LabelEncoder或OneHotEncoder进行转换。比如,可以使用LabelEncoder进行转换,代码如下:
```
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for col in feature_cols:
train_data[col] = le.fit_transform(train_data[col])
test_data[col] = le.transform(test_data[col])
```
这段代码可以将train_data和test_data中的特征变量转换为数字类型的数据。注意,对于测试数据集,需要使用训练数据集上的LabelEncoder进行转换,而不是重新创建一个新的LabelEncoder。转换完成后,再运行决策树模型,即可得到正确的结果。