Python第六章实训
时间: 2023-12-14 08:33:09 浏览: 71
Python第六章实训主要是使用scikit-learn构建模型。具体内容包括以下几个方面:
1. 数据集的加载和划分
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# 加载数据集
iris = load_iris()
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=0)
```
2. 数据的标准化
```python
from sklearn.preprocessing import StandardScaler
# 标准化数据
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)
```
3. 特征的降维
```python
from sklearn.decomposition import PCA
# 降维
pca = PCA(n_components=2)
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.transform(X_test_std)
```
4. 模型的构建和训练
```python
from sklearn.linear_model import LogisticRegression
# 构建模型
lr = LogisticRegression(C=100.0, random_state=1)
# 训练模型
lr.fit(X_train_pca, y_train)
```
5. 模型的评估
```python
from sklearn.metrics import accuracy_score
# 预测测试集
y_pred = lr.predict(X_test_pca)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %.2f' % accuracy)
```