visualize\(evaluate\)overview$metric.heatmap

Metric heatmap across models

Description

Metric heatmap across models

Usage

visualize.evaluate.metric.heatmap(
  results,
  metrics = NULL,
  models = NULL,
  outcomes = NULL,
  show.values = c("none", "rank", "value"),
  digits = 2,
  rows.by = c("models", "metrics"),
  cols = c("#1b7837", "#f7f7f7", "#b2182b"),
  score.cols = c("#f7f7f7", "#1b7837"),
  ...
)

Arguments

`results`	A `PolyGeniusEvaluation` object.
`metrics`	Optional flat filter vector. Values may be analysis names and/or metric names. Analysis names expand to all metrics present in the object for that analysis. Similarity is always excluded.
`models`	Optional model filter.
`outcomes`	Optional outcome filter.
`show.values`	Cell text to print (`“none”`, `“rank”`, or `“value”`).
`digits`	Number of digits for value labels.
`rows.by`	Whether heatmap rows represent `models` or `metrics`.
`cols`	Rank heatmap colors from best rank to worst rank.
`score.cols`	Total score annotation colors from low to high score.
`…`	Additional arguments forwarded to the tidyHeatmap heatmap call.

Value

A tidyHeatmap/ComplexHeatmap heatmap.

See Also

Other visualize: visualize.evaluate.calibration.curve(), visualize.evaluate.calibration.slope.intercept(), visualize.evaluate.compare.delta.forest(), visualize.evaluate.compare.delta.heatmap(), visualize.evaluate.confusion(), visualize.evaluate.discrimination.curve(), visualize.evaluate.distribution(), visualize.evaluate.forest(), visualize.evaluate.incremental.delta(), visualize.evaluate.leaderboard(), visualize.evaluate.metric.flags(), visualize.evaluate.pr(), visualize.evaluate.predicted.vs.observed(), visualize.evaluate.residuals(), visualize.evaluate.risk.strata.lift(), visualize.evaluate.risk.strata.profile(), visualize.evaluate.roc(), visualize.evaluate.similarity.heatmap()