将wave数据集进行分箱处理,并在分箱特征上比较线性回归和决策树回归的代码
时间: 2024-10-21 07:07:48 浏览: 15
将Wave数据集进行分箱处理通常涉及将连续数值变量转换为离散类别,这有助于减少数据的复杂性和提高模型解释性。这里是一个简单的Python示例,使用pandas库进行分箱,然后分别用sklearn的`LinearRegression`和`DecisionTreeRegressor`进行线性回归和决策树回归:
```python
# 导入必要的库
import pandas as pd
from sklearn.datasets import load_waveform
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
# 加载Wave数据集
wave_data = load_waveform()
X, y = wave_data.data, wave_data.target
# 数据预处理
# 假设X是需要分箱的特征列
bin_size = 5 # 分箱大小
binning = KBinsDiscretizer(n_bins=bin_size, encode='ordinal') # 创建分箱器
X_binned = binning.fit_transform(X)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_binned, y, test_size=0.2, random_state=42)
# 线性回归模型
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred_lr = lr.predict(X_test)
# 决策树回归模型
dt = DecisionTreeRegressor(random_state=42)
dt.fit(X_train, y_train)
y_pred_dt = dt.predict(X_test)
# 计算并比较RMSE
mse_lr = mean_squared_error(y_test, y_pred_lr)
mse_dt = mean_squared_error(y_test, y_pred_dt)
print(f"线性回归 RMSE: {mse_lr}")
print(f"决策树回归 RMSE: {mse_dt}")
#
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