A feature with a finite set of possible values. For example, a feature whose values may only be animal , vegetable , or mineral is a discrete (or categorical) feature.
목차 목차 관련 글 Connectionist approaches to language “How can linguistic phenomena traditionally analyzed with discrete symbolic computation be analyzed with continu…” Bucketization “For example, instead of representing temperature as a single continuous floating-point feature, you could chop …” Feature crossing “A synthetic feature formed by "crossing" categorical or bucketed features.” Sparse features “A feature whose values are predominately zero or empty.” Example (machine learning) “Furthermore, the features in an example can also include synthetic features, such as feature crosses.” Numerical data “Representing a feature as numerical data indicates that the feature's values have a mathematical relationship t…” Minimum marketable feature “One possible optimization is to order the creation of MMFs so as to create the maximum value as early as possib…” Permutation feature importance “One key advantage of permutation feature importance is that it is model-agnostic, i.e. it can be applied to any…” Clipping (machine learning) “For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60.” Dense feature “A feature in which most or all values are nonzero, typically a tensor of floating-point values.” Animal communications “Some aspects of song learning are analogous to those documented for human speech, including involvement of part…” ML crash course - Linear regression “Weight is a parameter of the model and is calculated during training.” Feature envy “A classic case of Feature Envy occurs when a function in one module spends more time communicating with functio…” Introduction to machine learning problem framing 관련 절: Predictive ML and data Synthetic feature “A feature not present among the input features, but assembled from one or more of them.” Downsampling (machine learning) “Reducing the amount of information in a feature in order to train a model more efficiently.”