是否可以在 sklearn 中计算线性和多项式回归模型的置信区间？

Question

I am doing some basic forecasting using the sklearn linear and polynomial regression models. I am able to get my model up and running pretty quickly to make predictions, but I also would like to calculate the 80% confidence intervals to get the upper and lower bounds along with the predictions.

Is there an easy way to calculate those numbers using inbuilt sklearn features?

here is what my data df looks like:

+------------+------+
|    date    | cost |
+------------+------+
| 01/01/2022 |  100 |
| 02/01/2022 |  104 |
| 03/01/2022 |  107 |
| 04/01/2022 |  108 |
| 05/01/2022 |  111 |
| 06/01/2022 |  117 |
| 07/01/2022 |  120 |
| 08/01/2022 |  122 |
| 09/01/2022 |  128 |
| 10/01/2022 |  133 |
+------------+------+

For both models and for the next 10 days for example, I want to calculate yhat , yhat_lower , and yhat_upper , but I am not sure on the best way to do this. Any help would be appreciated!

Answer 1

import numpy as np
import matplotlib.pyplot as plt

# confidence level
confidence = .2

# your predictions
y_pred = np.array([100,104,107,108,111,117,120,122,128,133])

y_l, y_h = y_pred * (1 - confidence), y_pred * (1 + confidence)

fig, ax = plt.subplots()
ax.plot(range(len(y_pred)), y_pred, color="green")
ax.fill_between(range(len(y_pred)), y_l, y_h, color='b', alpha=.1)
plt.show()