Sign function
The sign function maps a numeric value to an indication of whether it is negative, zero, or positive, commonly represented as −1, 0, or 1.
Machine Learning Algorithms in Depth
A sign function returns the sign of a numeric input, commonly mapping positive and negative values to +1 and −1.
- How it is used
- Maps a perceptron’s linear score to binary predictions for classification.
- How deep it goes
- Defines the binary mapping and connects it to a hyperplane, without developing the function beyond linear classification.
- Examples
- Uses a perceptron classifier on the iris dataset and illustrates its two-dimensional decision boundary.
- What you can do after
- Evaluate a linear score as a binary classification prediction.
Read how Machine Learning Algorithms in Depth explains sign function →
Math and Architectures of Deep Learning
The positive, negative, or zero indicator of a numeric expression, used here to determine which side of a decision boundary a point lies on.
- How it is used
- Determines which side of a binary-classification decision boundary contains an input point.
- How deep it goes
- Defines negative, zero, and positive values geometrically, extending from lines to higher-dimensional surfaces.
- Examples
- Uses the line y + 2x + 1 = 0 and classifier planes to interpret point locations.
- What you can do after
- Evaluate a surface function’s sign to predict a binary class for an input vector.
Read how Math and Architectures of Deep Learning explains sign function →
Related concepts
- floating-point arithmeticpart of · Floating-point arithmetic is computation using finite-precision representations of real numbers, which can introduce rounding and range errors.
- decision boundaryuses · A decision boundary is the locus in input or feature space that separates regions assigned different classes or decision outcomes by a predictive model.
- sign bitimplemented by · A sign bit is a designated bit in a numeric representation that indicates whether the represented value is negative or nonnegative.
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