Predicted Probability, Explained: A Visual Guide with Code Examples for Beginners
MODEL EVALUATION & OPTIMIZATION
Classification models don't just tell you what they think the answer is – they also tell you how sure they are about that answer. This certainty is shown as a probability score. A high score means the model is very confident, while a low score means it's uncertain about its prediction.
Every classification model calculates these probability scores differently. Simple models and complex ones each have their own specific methods to determine the likelihood of each possible outcome.
We're going to explore seven basic classification models and visually break down how each one figures out its probability scores. No need for a crystal ball – we'll make these probability calculations crystal clear!
Definition
Predicted probability (or "class probability") is a number from 0 to 1 (or 0% to 100%) that shows how confident a model is about its answer. If the number is 1, the model is completely sure about its answer. If it's 0.5, the model is basically guessing – it's like flipping a coin.
Components of a Probability Score
When a model has to choose between two classes (called binary classification), three main rules apply:
- The predicted probability must be between 0 and 1
- The chances of both options happening must add up to 1
- A higher probability means the model is more sure about its choice
For binary classification, when we talk about predicted probability, we usually mean the probability of the positive class. A higher probability means the model thinks the positive class is more likely, while a lower probability means it thinks the negative class is more likely.
To make sure these rules are followed, models use mathematical functions to convert their calculations into proper probabilities. Each type of model might use different functions, which affects how they express their confidence levels.
Prediction vs. Probability
In classification, a model picks the class it thinks will most likely happen – the one with the highest probability score. But two different models might pick the same class while being more or less confident about it. Their predicted probability scores tell us how sure each model is, even when they make the same choice.
These different probability scores tell us something important: even when models pick the same class, they might understand the data differently.
One model might be very sure about its choice, while another might be less confident – even though they made the same prediction.
