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fastml vs gghighlight

A side-by-side editorial comparison of fastml and gghighlight — release velocity, themes, recent moves, and the top alternatives to consider.

fastml vs gghighlight: at a glance

Featurefastmlgghighlight
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesautoml, tidymodels, survival analysis, cross-validationggplot2, data-visualisation, ggplot-extension, upstream-compat
Last editorial update3h ago1h ago
WebsiteVisit →Visit →

What is fastml?

fastml added survival modelling and leakage-proof resampling, moving past classification and regression.

A tidymodels-based AutoML wrapper that trains, tunes and compares many engines from one call. The 0.6.x line added engine-specific tuning parameters, class-imbalance handling, early stopping and DALEX-based explainability. The 0.7.5 release is far larger: a full survival analysis task with its own engines, MICE imputation and integrated Brier scoring, plus unbiased nested cross-validation, grouped, blocked and rolling resampling helpers, fold-wise imputation, recipe leakage checks, and a sandbox for user-supplied preprocessing.

Read the full fastml trajectory →

What is gghighlight?

A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.

gghighlight adds one verb to ggplot2: highlight the series matching a predicate and grey out the rest, with unhighlighted_params controlling how the shadowed layer renders and calculate_per_facet deciding whether the predicate evaluates within facets. The API settled at 0.2.0; the 0.5.0 release supports ggplot2 v4.0 including its ink and paper theme elements, and finally deletes gghighlight_point() and gghighlight_line().

Read the full gghighlight trajectory →

fastml vs gghighlight: editorial side-by-side

F
fastml
ANALYTICS
0.0

fastml added survival modelling and leakage-proof resampling, moving past classification and regression.

◆ Current state

A tidymodels-based AutoML wrapper that trains, tunes and compares many engines from one call. The 0.6.x line added engine-specific tuning parameters, class-imbalance handling, early stopping and DALEX-based explainability. The 0.7.5 release is far larger: a full survival analysis task with its own engines, MICE imputation and integrated Brier scoring, plus unbiased nested cross-validation, grouped, blocked and rolling resampling helpers, fold-wise imputation, recipe leakage checks, and a sandbox for user-supplied preprocessing.

◆ Where it's heading

The package is moving from convenience wrapper to something that has to be defensible statistically. Nested cross-validation, fold-wise rather than up-front imputation, and explicit leakage checks are all corrections to the shortcuts that make AutoML easy and its scores optimistic. Survival adds a third task type alongside classification and regression, and it arrived with its own metrics rather than being bolted onto the existing ones. Note the entry body is cut off at 8,000 characters, so the release is larger than what is shown.

◆ Prediction

Expect the remaining survival engines to fill in and the sandboxing of custom preprocessing to tighten, since both were still being iterated on within this same release's commit list.

G
gghighlight
ANALYTICS
0.0

A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.

◆ Current state

gghighlight adds one verb to ggplot2: highlight the series matching a predicate and grey out the rest, with unhighlighted_params controlling how the shadowed layer renders and calculate_per_facet deciding whether the predicate evaluates within facets. The API settled at 0.2.0; the 0.5.0 release supports ggplot2 v4.0 including its ink and paper theme elements, and finally deletes gghighlight_point() and gghighlight_line().

◆ Where it's heading

Two threads run through the history. One is a slow deprecation, from soft-deprecating the geom-specific functions at 0.1.0, to defunct at 0.3.0, to removed at 0.5.0 — a five-year removal cycle. The other is compatibility work: purrr 1.0.0, dplyr's across() deprecation, ggplot2 3.4.0, then 4.0. Genuine feature additions are rare and small, with line_label_type at 0.4.0 the last one. Note that 0.3.2's notes restate 0.3.1's n() item, so adjacent tags here overlap rather than each describing distinct work.

◆ Prediction

The next release most likely absorbs further ggplot2 4.x changes, given that is what triggered the last three. Nothing in the entries points to a new highlighting capability.

Alternatives to fastml and gghighlight

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either fastml or gghighlight.

See all fastml alternatives → · See all gghighlight alternatives →

Recent activity from fastml and gghighlight

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 8mo agofastmlVersion 0.7.5
  2. 1y agofastmlEngine-specific tuning, imbalance handling and explainability
  3. 1y agogghighlightggplot2 v4.0 support; geom-specific functions removed
  4. 1y agofastmlSingle-workflow evaluation fix
  5. 1y agofastmlVersion 0.5.0
  6. 2y agogghighlightTest expectations updated for upcoming ggplot2
  7. 3y agogghighlightline_label_type adds geomtextpath and second-axis labelling
  8. 4y agogghighlightDeprecated dplyr::across() usage removed
  9. 5y agogghighlightExplicit NULL in unhighlighted_params preserved; aesthetic name clash fixed
  10. 5y agogghighlightDiscrete-scale labels and n() predicates

Frequently asked questions

What is the difference between fastml and gghighlight?

They serve adjacent needs but don't currently overlap on shipped themes. fastml and gghighlight are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is fastml better than gghighlight?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastml and gghighlight are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to fastml?

Top fastml alternatives in Analytics are ranked by recent ship velocity. Browse the "fastml alternatives" section above for the current picks, or visit /alternatives/fastml for the full list with editorial commentary on each.

What are the best alternatives to gghighlight?

Top gghighlight alternatives in Analytics are ranked by recent ship velocity. Browse the "gghighlight alternatives" section above for the current picks, or visit /alternatives/gghighlight for the full list with editorial commentary on each.