调用sklearn库实现对鸢尾花数据的分类,采用逻辑回归分类方法,并计算以下多分类性能评价指标(两种方式):混淆矩阵、准确率、精确率、召回率、F1值、分类报告,并绘制PR曲线与ROC曲线。
时间: 2023-12-24 16:05:25 浏览: 41
好的,让我们开始回答这个问题。首先,我们需要导入必要的库:
```python
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import precision_recall_curve, roc_curve, auc
import matplotlib.pyplot as plt
import numpy as np
```
接下来,我们需要加载鸢尾花数据集并进行预处理:
```python
# 加载数据集
iris = load_iris()
# 获取特征和标签
X = iris.data
y = iris.target
# 将数据集拆分成训练集和测试集
np.random.seed(42)
indices = np.random.permutation(len(X))
X_train = X[indices[:-30]]
y_train = y[indices[:-30]]
X_test = X[indices[-30:]]
y_test = y[indices[-30:]]
```
然后,我们需要训练一个逻辑回归模型并对测试集进行预测:
```python
# 训练逻辑回归模型
clf = LogisticRegression(random_state=42, multi_class='multinomial', solver='newton-cg')
clf.fit(X_train, y_train)
# 对测试集进行预测
y_pred = clf.predict(X_test)
```
接下来,我们可以使用混淆矩阵计算模型的性能指标:
```python
# 计算混淆矩阵
cm = confusion_matrix(y_test, y_pred)
print(cm)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
# 计算精确率
precision = precision_score(y_test, y_pred, average='macro')
print(f"Precision: {precision:.2f}")
# 计算召回率
recall = recall_score(y_test, y_pred, average='macro')
print(f"Recall: {recall:.2f}")
# 计算F1值
f1 = f1_score(y_test, y_pred, average='macro')
print(f"F1-score: {f1:.2f}")
```
除此之外,我们还可以使用分类报告函数来计算性能指标:
```python
# 计算分类报告
report = classification_report(y_test, y_pred)
print(report)
```
接下来,我们可以绘制PR曲线和ROC曲线:
```python
# 计算每个类别的概率和阈值
y_prob = clf.predict_proba(X_test)
precision = dict()
recall = dict()
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(3):
precision[i], recall[i], _ = precision_recall_curve(y_test, y_prob[:, i], pos_label=i)
fpr[i], tpr[i], _ = roc_curve(y_test, y_prob[:, i], pos_label=i)
roc_auc[i] = auc(fpr[i], tpr[i])
# 绘制PR曲线
plt.figure()
plt.clf()
plt.plot(recall[0], precision[0], linewidth=2, label='class 0 (area = %0.2f)' % roc_auc[0])
plt.plot(recall[1], precision[1], linewidth=2, label='class 1 (area = %0.2f)' % roc_auc[1])
plt.plot(recall[2], precision[2], linewidth=2, label='class 2 (area = %0.2f)' % roc_auc[2])
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall curve')
plt.legend(loc="lower left")
plt.show()
# 绘制ROC曲线
plt.figure()
plt.clf()
plt.plot(fpr[0], tpr[0], linewidth=2, label='class 0 (area = %0.2f)' % roc_auc[0])
plt.plot(fpr[1], tpr[1], linewidth=2, label='class 1 (area = %0.2f)' % roc_auc[1])
plt.plot(fpr[2], tpr[2], linewidth=2, label='class 2 (area = %0.2f)' % roc_auc[2])
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
```
好的,这就是使用sklearn库对鸢尾花数据进行分类并计算多分类性能评价指标的方法。希望我的回答对您有所帮助!