In [1]:
from dsc80_utils import *
import lec16_util as util

Lecture 16 – Hyperparameters, Cross-Validation, and Decision Trees¶

DSC 80, Spring 2024¶

Announcements 📣¶

  • Lab 8 is due on Wednesday.
  • Final Project Checkpoint 2 is due Friday.
  • The Final Exam is on Saturday, June 8 from 8-11AM (room TBD).
    • You can bring two double-sided notes sheets (you can bring your midterm notes sheet, if you want).
    • More details to come.

Agenda 📆¶

  • Hyperparameters.
  • Cross-validation.
  • Decision trees.

Question 🤔 (Answer at q.dsc80.com)

Remember, you can always ask questions at q.dsc80.com!

Review: Hyperparameters¶

In [2]:
np.random.seed(23) # For reproducibility.

def sample_from_pop(n=100):
    x = np.linspace(-2, 3, n)
    y = x ** 3 + (np.random.normal(0, 3, size=n))
    return pd.DataFrame({'x': x, 'y': y})

sample_1 = sample_from_pop()
sample_2 = sample_from_pop()
In [3]:
px.scatter(sample_1, x='x', y='y', title='Sample 1')