Machine Learning

2026-06-27

Type 1 error

Parametric Modelling

2026-06-28

This accounts for about 60% of the Machine Learning Methods we have.

By definition a parametric model is one that has fixed parameters to learn, i.e. weights in Linear Regression: \(w_0, w_1, …, w_n\). Conversely, a non-parametric model does not have a fixed number of parameters to learn: K-means clustering for example just clusters the data as best as it can.

We can list some more models:

  1. Linear Regression
  2. Ridge Regression
  3. Lasso Regression
  4. Logistic Regression
  5. Neural Networks
  6. Perceptron
  7. Naive Bayes

Predicting Life Expectancy

2026-06-28
linear‑regressionpandasloocvlassoridgeedahyperparameter‑tuning

Intro

The focus here is on EDA (Exploratory Data Analysis) and investigating the best choice for the \(\lambda\) hyperparameter for LASSO and Ridge Regression.

We will be working on the Life Expectancy CSV data obtained from WHO.

Peeking at Data

We begin by viewing the columns of the Life Expectancy Dataframe:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

pd.options.display.float_format = '{:.2f}'.format
le_df = pd.read_csv("life_expectancy.csv")
le_df.columns

We can then view the range of our life expectancy values with a box plot:

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