Hanami encoding specification for test metric error bands.
Displays the range (mean ± stddev) of test/validation metrics as an orange
error band. Y-axis spans from metric-test-min to metric-test-max, X-axis
is training dataset size.
Hanami encoding specification for test metric error bands. Displays the range (mean ± stddev) of test/validation metrics as an orange error band. Y-axis spans from `metric-test-min` to `metric-test-max`, X-axis is training dataset size.
Hanami encoding specification for training metric error bands.
Displays the range (mean ± stddev) of training metrics as a blue error band.
Y-axis spans from metric-train-min to metric-train-max, X-axis is training
dataset size.
Hanami encoding specification for training metric error bands. Displays the range (mean ± stddev) of training metrics as a blue error band. Y-axis spans from `metric-train-min` to `metric-train-max`, X-axis is training dataset size.
Hanami layer specification for learning curve visualization.
Combines three layers:
Creates a comprehensive learning curve showing both central tendency and variance.
Hanami layer specification for learning curve visualization. Combines three layers: 1. Training error band (blue, showing mean ± stddev) 2. Test error band (orange, showing mean ± stddev) 3. Line plot with points (showing mean metrics) Creates a comprehensive learning curve showing both central tendency and variance.
Hanami encoding specification for metric line plots.
Displays training and test metrics as separate colored lines (blue for training, orange for test). X-axis is training dataset size, Y-axis is the metric value. Legend distinguishes between training score and cross-validation metric.
Hanami encoding specification for metric line plots. Displays training and test metrics as separate colored lines (blue for training, orange for test). X-axis is training dataset size, Y-axis is the metric value. Legend distinguishes between training score and cross-validation metric.
(spec lc-vl-data)Creates a Hanami/Vega-Lite specification for a learning curve chart.
lc-vl-data - Dataset with columns: :train-ds-size, :metric-test,
:metric-train, :metric-test-min, :metric-test-max, :metric-train-min,
:metric-train-max
Returns a Hanami layer chart showing:
Use with vl-data to prepare data from learning curve results.
See also: scicloj.metamorph.ml/learning-curve
Creates a Hanami/Vega-Lite specification for a learning curve chart. `lc-vl-data` - Dataset with columns: `:train-ds-size`, `:metric-test`, `:metric-train`, `:metric-test-min`, `:metric-test-max`, `:metric-train-min`, `:metric-train-max` Returns a Hanami layer chart showing: * Training and test metrics over varying training set sizes * Error bands indicating variance (mean ± standard deviation) * Line plots with points for mean metric values Use with `vl-data` to prepare data from learning curve results. See also: `scicloj.metamorph.ml/learning-curve`
(vl-data lc-rf)Prepares learning curve data for Vega-Lite visualization.
lc-rf - Raw learning curve results dataset from scicloj.metamorph.ml/learning-curve
Returns an aggregated dataset grouped by :train-size-index with columns:
:metric-test, :metric-train - Mean metric values:metric-test-min, :metric-train-min - Mean minus standard deviation:metric-test-max, :metric-train-max - Mean plus standard deviation:train-ds-size, :test-ds-size - Rounded mean dataset sizesUse with spec to create a learning curve visualization.
Prepares learning curve data for Vega-Lite visualization. `lc-rf` - Raw learning curve results dataset from `scicloj.metamorph.ml/learning-curve` Returns an aggregated dataset grouped by `:train-size-index` with columns: * `:metric-test`, `:metric-train` - Mean metric values * `:metric-test-min`, `:metric-train-min` - Mean minus standard deviation * `:metric-test-max`, `:metric-train-max` - Mean plus standard deviation * `:train-ds-size`, `:test-ds-size` - Rounded mean dataset sizes Use with `spec` to create a learning curve visualization.
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