Discriminant Analysis Examples for Beginners

Explore practical examples of discriminant analysis across diverse fields.
By Jamie

Introduction to Discriminant Analysis

Discriminant Analysis is a statistical technique used to classify a set of observations into predefined classes. It helps in understanding the differences between groups and predicting the category of new observations based on known characteristics. This method is particularly useful in various fields such as finance, healthcare, and marketing. Below are three practical examples that illustrate the application of Discriminant Analysis.

Example 1: Classifying Species of Iris Flowers

Context

In the field of botany, identifying different species of flowers can be challenging. The Iris dataset, which includes measurements of iris flowers, serves as a classic example for demonstrating discriminant analysis.

Using measurements of petal length, petal width, sepal length, and sepal width, we can classify three species of iris: Setosa, Versicolor, and Virginica.

Example

  1. Data Collection: Gather data on the four measurements for each species of iris flower.
  2. Data Preparation: Organize the data into a table with columns for each measurement and a label for the species.
  3. Model Training: Use Linear Discriminant Analysis (LDA) to create a model based on the training dataset, which includes measurements from 100 iris flowers.
  4. Classification: Test the model on a new set of measurements to predict the species. For instance, if a flower has a petal length of 1.5 cm and a petal width of 0.2 cm, the model might predict it as Setosa.

Notes

  • This example showcases how LDA can effectively distinguish between multiple categories based on continuous variables.
  • Variations could include using Quadratic Discriminant Analysis (QDA), which allows for non-linear boundaries between classes.

Example 2: Credit Scoring in Banking

Context

In the banking sector, assessing the creditworthiness of loan applicants is crucial. Discriminant Analysis is often employed to classify applicants into categories such as ‘high risk’ and ‘low risk’ based on their financial attributes.

Example

  1. Data Collection: Collect data on various attributes such as income, credit history, loan amount requested, and existing debt.
  2. Data Preparation: Compile this data into a structured format, labeling each applicant as either ‘Approved’ or ‘Rejected’ based on past decisions.
  3. Model Training: Implement LDA to analyze historical data and identify the characteristics associated with each risk category.
  4. Classification: Apply the model to new applicants. For instance, an applicant with a high income but a low credit score may be classified as ‘high risk’, while another with a steady income and good credit history could be labeled as ‘low risk’.

Notes

  • The results can help financial institutions make informed lending decisions, reducing the likelihood of defaults.
  • It’s essential to consider the ethical implications of using such models to avoid bias in classification.

Example 3: Customer Segmentation in Marketing

Context

In marketing, understanding customer segments helps businesses tailor their strategies to meet different needs. Discriminant Analysis can categorize customers based on purchasing behavior and demographics.

Example

  1. Data Collection: Gather data on customer demographics (age, income, location) and purchasing behavior (frequency of purchases, average spend).
  2. Data Preparation: Organize the data into groups such as ‘Frequent Buyers’, ’Occasional Buyers’, and ’Non-Buyers’.
  3. Model Training: Use LDA to analyze the features of each group and identify patterns that differentiate them.
  4. Classification: Apply the model to a new dataset of customers. For instance, if a new customer is a 30-year-old with high income and frequent online purchases, the model might classify them as a ‘Frequent Buyer’.

Notes

  • This example highlights the marketing potential of Discriminant Analysis for targeted advertising and personalized offers.
  • Companies can also explore incorporating additional variables like customer satisfaction ratings to enhance the model’s accuracy.