pipeFilter: Filter 'ExprsPipeline' Object
In tpq/exprso: Rapid Deployment of Machine Learning Algorithms

Description Usage Arguments Details Value

pipeFilter subsets an ExprsPipeline object.

1	pipeFilter(object, colBy, how = 0, gate = 0, top = 0)

`object`	An `ExprsPipeline-class` object.
`colBy`	A character vector or string. Specifies column(s) to use when filtering by model performance. Listing multiple columns will result in a filter based on the product all listed columns.
`how, gate`	A numeric scalar. Arguments between 0 and 1 will impose a threshold or ceiling filter, respectively, based on the raw value of `colBy`. Arguments between 1 and 100 will impose a filter based on the percentile of `colBy`. The user may also provide "midrange", "median", or "mean" as an argument for these filters.
`top`	A numeric scalar. Determines the top N models based on `colBy` to include after the threshold and ceiling filters. In the case that the `@summary` slot contains the column "boot", this selects the top N models for each unique bootstrap.

The filter process occurs in three steps. However, the user may skip any one of these steps by setting the respective argument to 0. First, a threshold filter gets imposed. Any model with a performance less than the threshold filter, how, gets excluded. Second, a ceiling filter gets imposed. Any model with a performance greater than the ceiling filter, gate, gets excluded. Third, an arbitrary subset occurs. The top N models in the ExprsPipeline object get selected based on the argument top. However, in the case that the @summary slot contains the column "boot", pipeFilter selects the top N models per bootstrap.

pipeFilter will apply this filter based on the performance metrics listed in the colBy argument. Listing multiple columns will result in a filter based on the product of all listed columns. To weigh one metric over another, list that column more times.

An ExprsPipeline-class object.

tpq/exprso documentation built on July 27, 2019, 8:44 a.m.