tech.xgboost.dataset — techascent/tech.xgboost 1.0

average-prediction-error clj

(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/

dataset->dmatrix clj

(dataset->dmatrix dataset feature-keys label-key)

Dataset is a sequence of maps. Each contains a feature key. Returns a dmatrix.

dataset->k-fold-datasets clj

(dataset->k-fold-datasets dataset k)

dataset->labeled-point-iterator clj

(dataset->labeled-point-iterator dataset
                                 feature-keys
                                 label-key
                                 &
                                 {:keys [weight group]
                                  :or {weight 1.0 group -1}})

Create an iterator to labeled points from a possibly quite large sequence of maps. Sets expected length to length of first entry

dataset-seq->dataset-model-seq clj

(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

get-dataset-item clj

(get-dataset-item dataset-values item-key)