Basic Math Concepts – Classification vs Regression

1. Basic Arithmetic

  • Addition, subtraction, multiplication, division
  • Percentages and ratios : Why? To understand features like “frequency of purchases” or “average salary”

2. Understanding Variables

  • Independent variable: Input (e.g., age, experience)
  • Dependent variable: Output (e.g., price or label)

Why? Both classification and regression models map inputs (X) to outputs (Y)

3. Coordinate Geometry (2D plotting)

  • X-axis, Y-axis
  • Points on a graph

Why? Visualizing how inputs relate to outputs (e.g., house size vs. price)

4. Basic Statistics

  • Mean, median, mode
  • Variance and standard deviation
  • Concept of distribution (e.g., normal distribution)

Why? Helps understand how data is spread, and how models “fit” data.

5. Logical Reasoning

  • Yes/No decisions
  • Grouping similar things

Why? Classification relies heavily on logic: “Is it A, B, or C?”

6. Basic Probability (Optional for classification)

  • What is the chance something belongs to a class?
  • Events and likelihood

Why? Used in classifiers like Naive Bayes, or confidence estimation

Next – Logistic Regression