#AIGlossary

categorical data

#fundamentals
Features having a specific set of possible values. For example, consider a categorical feature named traffic-light-state, which can only have one of the following three possible values:

red
yellow
green

By representing traffic-light-state as a categorical feature, a model can learn the differing impacts of red, green, and yellow on driver behavior.

Categorical features are sometimes called discrete features.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#categorical-data

#AIGlossary

candidate sampling

A training-time optimization that calculates a probability for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For instance, given an example labeled beagle and dog, candidate sampling computes the predicted probabilities and corresponding loss terms for:

- beagle
- dog
- a random subset of the remaining negative classes (for example, cat, lollipop, fence).

#GoogleCloud

https://developers.google.com/machine-learning/glossary#candidate-sampling

#AIGlossary

Candidate generation

The initial set of recommendations chosen by a recommendation system. For example, consider a bookstore that offers 100,000 titles. The candidate generation phase creates a much smaller list of suitable books for a particular user, say 500. But even 500 books is way too many to recommend to a user. Subsequent, more expensive, phases of a recommendation system reduce those 500 to a much smaller and useful recommendations.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#candidate-generation

#AIGlossary

Candidate generation

The initial set of recommendations chosen by a recommendation system. For example, a 100,000 titles bookstore.

The candidate generation phase creates a much smaller list of suitable books for a particular user, say 500. But even 500 books is way too many to recommend to a user.

Subsequent, more expensive, phases of a recommendation system reduce those 500 to a much smaller, more useful set of recommendations.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#candidate-generation

#AIGlossary

Calibration layer

A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#calibration-layer

#AIGlossary

Bucketing

#fundamentals

Converting a single feature into multiple binary features called buckets or bins, typically based on a value range. The chopped feature is typically a continuous feature.

For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete buckets.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#bucketing

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Broadcasting

Expanding the shape of an operand in a matrix math operation to dimensions compatible for that operation.

For example, linear algebra requires that the two operands in a matrix addition operation must have the same dimensions.

Consequently, you can't add a matrix of shape (m, n) to a vector of length n.

Broadcasting enables this operation by virtually expanding the vector of length n to a matrix of shape (m, n).

#GoogleCloud

https://developers.google.com/machine-learning/glossary#broadcasting

#AIGlossary

Bounding box

In an image, the (x, y) coordinates of a rectangle around an area of interest, such as the dog in the image below.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#bounding-box

#AIGlossary

Boosting

A machine learning technique that iteratively combines a set of simple and not very accurate classification models (referred to as "weak classifiers") into a classification model with high accuracy (a "strong classifier") by upweighting the examples that the model is currently misclassifying.

#GoogleCloud

https://developers.google.com/machine-learning/glossary#boosting

"Claude can't replace taste or imagination, but it can open up new ways of working—faster and more ambitious ideation, a more expansive skill set, and the ability for creatives to take on larger-scale projects"

https://www.anthropic.com/news/claude-for-creative-work