Given a datset which must have exactly 1 inference target column return a default loss fn. If column is categorical, loss is tech.v3.ml.loss/classification-loss, else the loss is tech.v3.ml.loss/mae (mean average error).

Create a model definition. An ml model is a function that takes a dataset and an options map and returns a model. A model is something that, combined with a dataset, produces a inferred dataset.

Explain (if possible) an ml model. A model explanation is a model-specific map of data that usually indicates some level of mapping between features and importance

Get the hyperparameters for this model definition

Return a list of all registered model defintion names.

Map of model kwd to model definition

Return the model definition that corresponse to the :model-type option

Predict returns a dataset with only the predictions in it.

• For regression, a single column dataset is returned with the column named after the target
• For classification, a dataset is returned with a float64 column for each target value and values that describe the probability distribution.

Thaw a model. Model’s returned from train may be ‘frozen’ meaning a ‘thaw’ operation is needed in order to use the model. This happens for you during preduct but you may also cached the ‘thawed’ model on the model map under the ‘:thawed-model’ keyword in order to do fast predictions on small datasets.

Given a dataset and an options map produce a model. The model-type keyword in the options map selects which model definition to use to train the model. Returns a map containing at least:

• :model-data - the result of that definitions’s train-fn.
• :options - the options passed in.
• :id - new randomly generated UUID.
• `:feature-columns - vector of column names.
• `:target-columns - vector of column names.

Train a model gridsearching across the options map. The gridsearch map is built by merging the model’s hyperparameter definitions into the options map. If the sobol sequence returned has only one element a warning is issued. Note this returns a sequence of models as opposed to a single model.

• Searches across k-fold datasets if n-k-folds is > 1. n-k-folds defaults to 5.
• Searches (in parallel) through n-gridsearch option maps created via sobol-gridsearch.
• Returns n-result-models (defaults to 5) sorted by avg-loss.
• loss-fn can be provided or is the loss-fn returned via default-loss-fn.

Train a model across k-fold datasets using tech.v3.dataset.modelling/k-fold-dataset and then calculate the min,max,and avg across results using loss-fn. Adds :n-k-folds, :min-loss, :max-loss, :avg-loss and :loss (min-loss) to the model with the lowest loss.

• n-k-folds defaults to 5.
• loss-fn defaults to loss/mae if target column is not categorical else defaults to loss/classification-loss.

Train a model splitting the dataset using tech.v3.dataset.modelling/train-test-split and then calculate the loss using loss-fn. Loss is added to the model map under :loss.

• loss-fn defaults to loss/mae if target column is not categorical else defaults to loss/classification-loss.