Indicator function
An indicator function is a function that equals one when a specified condition holds and zero otherwise.
Machine Learning Algorithms in Depth
An indicator function returns one when a stated condition is true and zero otherwise.
- How it is used
- Converts geometric membership and classification mistakes into values that can be averaged or counted.
- How deep it goes
- Defines the notation, derives its role in a Monte Carlo pi estimate, and implements that estimate with a Boolean array.
- Examples
- Uses points inside a circle, a NumPy pi estimator, and mismatches between predicted and true labels.
- What you can do after
- Build it from scratch to estimate pi from uniform samples or compute a classification mistake rate from labels.
Read how Machine Learning Algorithms in Depth explains indicator function →
Causal AI
An indicator function returns one when a specified condition holds and zero otherwise.
- How it is used
- Uses thresholded uniform randomness to construct deterministic assignments inside structural causal models.
- How deep it goes
- Defines binary thresholding and explains its SCM role, but stops short of formal implementation beyond the assignment equation.
- Examples
- Demonstrates coin selection through stick breaking, uniform sampling, probability regions, and repeated Bernoulli outcomes.
- What you can do after
- Build it from scratch by replacing a Bernoulli draw with uniform sampling and an indicator assignment.
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