In [1]:
import pandas as pd
import numpy as np
import os

import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
pd.options.plotting.backend = 'plotly'
TEMPLATE = 'seaborn'

import warnings
warnings.simplefilter('ignore')

Lecture 25 – Grid Search, Multicollinearity, Examples¶

DSC 80, Spring 2023¶

Agenda¶

  • Recap: Decision trees 🌲 and grid search.
  • Multicollinearity (including how it arises in one hot encoding).
  • Example: Modeling with text features.

Recap: Decision trees 🌲 and grid search¶

Example: Predicting diabetes¶

In [2]:
diabetes = pd.read_csv('data/diabetes.csv')
diabetes.head()
Out[2]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
In [3]:
fig = (
    diabetes.assign(Outcome=diabetes['Outcome'].astype(str))
            .plot(kind='scatter', x='Glucose', y='BMI', color='Outcome', 
                  color_discrete_map={'0': 'orange', '1': 'blue'},
                  title='Relationship between Glucose, BMI, and Diabetes',
                  template=TEMPLATE)
)
fig