如何使用sklearn包分别采用KNN,决策树,随机森林,朴素贝叶斯, SVM, XGboost (最后两个选做)等对汽车消费群体的客户进行分类,并且显示分类结果
时间: 2024-03-20 13:42:55 浏览: 14
首先,需要将数据集划分为训练集和测试集。可以使用train_test_split函数从sklearn.model_selection中导入。
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
其中,X是特征矩阵,y是目标变量。test_size表示测试集所占比例,random_state表示随机种子,保证每次划分的结果相同。
然后,分别导入需要使用的分类器,并进行训练和预测。
```python
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from xgboost import XGBClassifier
# KNN
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
# 决策树
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)
# 随机森林
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
# 朴素贝叶斯
nb = GaussianNB()
nb.fit(X_train, y_train)
nb_pred = nb.predict(X_test)
# SVM
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
# XGboost
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
xgb_pred = xgb.predict(X_test)
```
最后,可以使用sklearn.metrics中的分类指标,如准确率、精确率、召回率和F1值等来评估分类器的性能。
```python
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print("KNN Accuracy:", accuracy_score(y_test, knn_pred))
print("Decision Tree Accuracy:", accuracy_score(y_test, dt_pred))
print("Random Forest Accuracy:", accuracy_score(y_test, rf_pred))
print("Naive Bayes Accuracy:", accuracy_score(y_test, nb_pred))
print("SVM Accuracy:", accuracy_score(y_test, svm_pred))
print("XGboost Accuracy:", accuracy_score(y_test, xgb_pred))
```
此外,可以使用sklearn.metrics中的classification_report函数来打印出分类报告,其中包含了准确率、精确率、召回率和F1值等各项指标。
```python
from sklearn.metrics import classification_report
print("KNN Classification Report:\n", classification_report(y_test, knn_pred))
print("Decision Tree Classification Report:\n", classification_report(y_test, dt_pred))
print("Random Forest Classification Report:\n", classification_report(y_test, rf_pred))
print("Naive Bayes Classification Report:\n", classification_report(y_test, nb_pred))
print("SVM Classification Report:\n", classification_report(y_test, svm_pred))
print("XGboost Classification Report:\n", classification_report(y_test, xgb_pred))
```
其中,y_test是测试集的真实标签,knn_pred、dt_pred、rf_pred、nb_pred、svm_pred和xgb_pred分别是KNN、决策树、随机森林、朴素贝叶斯、SVM和XGboost的预测结果。