Sign function

As explained in 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 Machine Learning Algorithms in Depth covers it

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 it assumes

Uses feature vectors, parameters, inner products, offsets, and binary labels in {+1, −1}.

What you can do after

Evaluate a linear score as a binary classification prediction.

How it connects

Connects sign function to perceptrons, linear classifiers, inner products, hyperplanes, decision boundaries, and binary labels.

liveBookmanning.com

Read Machine Learning Algorithms in Depth on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Math and Architectures of Deep LearningThe positive, negative, or zero indicator of a numeric expression, used here to determine which side of a decision boundary a point lies on.

Compare how 2 books explain 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.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.