Scikit-Learn的基础用法
时间: 2024-04-08 19:35:27 浏览: 23
Scikit-Learn是一个功能强大的机器学习库,提供了许多常用的机器学习算法和工具。下面是一些Scikit-Learn的基础用法:
1. 导入库和数据集:
```python
import sklearn
from sklearn import datasets
# 加载数据集
iris = datasets.load_iris()
```
2. 数据集划分:
```python
from sklearn.model_selection import train_test_split
# 划分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
```
3. 创建模型和训练:
```python
from sklearn.linear_model import LogisticRegression
# 创建模型
model = LogisticRegression()
# 训练模型
model.fit(X_train, y_train)
```
4. 预测和评估:
```python
from sklearn.metrics import accuracy_score
# 预测
y_pred = model.predict(X_test)
# 评估准确率
accuracy = accuracy_score(y_test, y_pred)
```
5. 交叉验证:
```python
from sklearn.model_selection import cross_val_score
# 交叉验证
scores = cross_val_score(model, iris.data, iris.target, cv=5)
```
6. 超参数调优:
```python
from sklearn.model_selection import GridSearchCV
# 定义超参数空间
param_grid = {'C': [1, 10, 100], 'penalty': ['l1', 'l2']}
# 网格搜索
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)
# 获取最佳参数和模型
best_params = grid_search.best_params_
best_model = grid_search.best_estimator_
```
这只是Scikit-Learn的一小部分功能和用法,你可以根据具体的问题和需求进一步探索和学习更多内容。