帮我写代码,构建AdaBoostClassifier,并完成训练,输出X_test上的预测结果(分类结果、概率结果),输出混淆矩阵以及预测数据上正确率。
时间: 2023-12-10 21:38:27 浏览: 23
好的,首先需要准备数据集,然后导入必要的库:
```python
import numpy as np
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import train_test_split
```
假设我们有一个名为 `data.csv` 的数据集,其中包含了许多特征及其对应的标签。我们可以使用 `pandas` 库读取这个数据集:
```python
data = pd.read_csv('data.csv')
```
接下来,我们需要将数据集分成训练集和测试集。我们可以使用 `train_test_split` 函数来完成这个任务:
```python
X = data.drop('label', axis=1)
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
这里将数据集按照 8:2 的比例划分成了训练集和测试集,其中 `random_state` 参数用于设置随机数种子,以保证每次运行的结果都一致。
接下来,我们需要构建 AdaBoostClassifier 并完成训练。在构建 AdaBoostClassifier 之前,我们需要确定一些参数,例如基分类器类型、基分类器数量、学习率等等。这里我们选择决策树作为基分类器,并设置基分类器数量为 100,学习率为 0.1:
```python
n_estimators = 100
learning_rate = 0.1
base_estimator = DecisionTreeClassifier(max_depth=1)
model = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=n_estimators, learning_rate=learning_rate)
model.fit(X_train, y_train)
```
在训练完成后,我们可以使用 `model.predict` 函数来对测试集进行预测,并计算分类结果、概率结果、混淆矩阵以及正确率:
```python
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:, 1]
cm = confusion_matrix(y_test, y_pred)
acc = accuracy_score(y_test, y_pred)
print('分类结果:', y_pred)
print('概率结果:', y_prob)
print('混淆矩阵:', cm)
print('正确率:', acc)
```
最后,完整代码如下:
```python
import numpy as np
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import train_test_split
data = pd.read_csv('data.csv')
X = data.drop('label', axis=1)
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
n_estimators = 100
learning_rate = 0.1
base_estimator = DecisionTreeClassifier(max_depth=1)
model = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=n_estimators, learning_rate=learning_rate)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:, 1]
cm = confusion_matrix(y_test, y_pred)
acc = accuracy_score(y_test, y_pred)
print('分类结果:', y_pred)
print('概率结果:', y_prob)
print('混淆矩阵:', cm)
print('正确率:', acc)
```