sklearn怎么使用
时间: 2024-04-29 17:25:32 浏览: 7
使用sklearn库可以进行机器学习模型的训练和预测。具体步骤如下:
1. 导入所需模块
```python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
```
2. 加载数据集
```python
boston = datasets.load_boston()
X = boston.data
y = boston.target
```
3. 数据集划分
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
4. 建立模型
```python
model = LinearRegression()
```
5. 模型训练
```python
model.fit(X_train, y_train)
```
6. 模型预测
```python
y_pred = model.predict(X_test)
```
7. 模型评估
```python
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean squared error: %.2f" % mse)
print("R2 score: %.2f" % r2)
```
以上就是使用sklearn进行机器学习的基本流程。可以根据不同的需求选择不同的模型和评估指标。