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tech.ml.train


average-prediction-errorclj

(average-prediction-error train-fn predict-fn label-key loss-fn dataset-seq)

Average prediction error across models generated with these datasets Page 242, https://web.stanford.edu/~hastie/ElemStatLearn/

Average prediction error across models generated with these datasets
Page 242, https://web.stanford.edu/~hastie/ElemStatLearn/
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dataset-seq->dataset-model-seqclj

(dataset-seq->dataset-model-seq train-fn dataset-seq)

Given a sequence of {:train-ds ...} datasets, produce a sequence of: {:model ...} train-ds is removed to keep memory usage as low as possible. See dataset/dataset->k-fold-datasets

Given a sequence of {:train-ds ...} datasets, produce a sequence of:
{:model ...}
train-ds is removed to keep memory usage as low as possible.
See dataset/dataset->k-fold-datasets
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find-best-optionsclj

(find-best-options train-fn
                   predict-fn
                   label-key
                   loss-fn
                   {:keys [parallelism]
                    :or {parallelism (.availableProcessors
                                       (Runtime/getRuntime))}}
                   option-seq
                   dataset-seq)

Given a sequence of options and a sequence of datasets (for k-fold), run them and return the best options. train-fn: (train-fn options dataset) -> model predict-fn: (predict-fn options dataset) -> prediction-sequence label-key: key to get labels from dataset. loss-fn: (loss-fn label-sequence prediction-sequence)-> double Lowest number wins.

Given a sequence of options and a sequence of datasets (for k-fold),
run them and return the best options.
train-fn: (train-fn options dataset) -> model
predict-fn: (predict-fn options dataset) -> prediction-sequence
label-key: key to get labels from dataset.
loss-fn: (loss-fn label-sequence prediction-sequence)-> double
  Lowest number wins.
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options-seqclj

(options-seq base-options parameter-sequence-map)

Given base options map and a map of parameter keyword -> value sequence produce a sequence of options maps that does a cartesian join across all of the parameter sequences

Given base options map and a map of parameter keyword -> value sequence
produce a sequence of options maps that does a cartesian join across all of
the parameter sequences
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