A machine learning model is a mathematical representation or algorithm that is trained to make predictions or decisions based on data. The model “learns” from the data during a training process and then uses this learning to make predictions or decisions on new, unseen data.
Example: Predicting Exam Scores Based on Study Hours
Goal:
We want to predict a student’s exam score based on the number of hours they studied.
Imagine we have a small dataset of students, where we know how many hours they studied and what score they got on their exam. For example, one student studied for 2 hours and scored 50, another studied for 4 hours and scored 60, and so on.
We use a Linear Regression model, which tries to draw a straight line through the data points to capture the relationship between “Hours Studied” and “Exam Score.”
The model learns that as the number of hours studied increases, the exam score also increases. Let’s say the model figures out the equation:
Exam Score = 5 × Hours Studied + 40This means the model thinks that for every extra hour of studying, the score increases by 5 points. Even if a student didn’t study at all (0 hours), they might still get 40 points (maybe from guessing!).
Now, with the model trained, we can use it to predict a student’s score based on the hours they studied. For example, if a student studied for 7 hours, the predicted score would be:
Predicted Score = 5 × 7 + 40 = 75So, the model predicts that this student will score 75 on the exam.
This example shows how a simple Linear Regression model can learn the relationship between hours studied and exam scores and then use that knowledge to predict future scores. It’s a basic and easy-to-understand way of demonstrating how machine learning models work.