RemixAutoML (version 0.4.8)

AutoRecommenderScoring: The AutoRecomScoring function scores recommender models from AutoRecommender()

Description

This function will take your ratings matrix and model and score your data in parallel.

This function will take your ratings matrix and model and score your data in parallel.

Usage

AutoRecommenderScoring(
  data,
  WinningModel,
  EntityColName = "CustomerID",
  ProductColName = "StockCode",
  NumItemsReturn = 1
)
AutoRecommenderScoring(
  data,
  WinningModel,
  EntityColName = "CustomerID",
  ProductColName = "StockCode",
  NumItemsReturn = 1
)

Arguments

data

The binary ratings matrix from RecomDataCreate()

WinningModel

The winning model returned from AutoRecommender()

EntityColName

Typically your customer ID

ProductColName

Something like "StockCode"

NumItemsReturn

Number of items to return on scoring

Value

Returns the prediction data

Returns the prediction data

See Also

Other Recommenders: AutoMarketBasketModel(), AutoRecomDataCreate(), AutoRecommender()

Other Recommenders: AutoMarketBasketModel(), AutoRecomDataCreate(), AutoRecommender()

Examples

# NOT RUN {
Results <- AutoRecommenderScoring(
  data = AutoRecomDataCreate(
      data,
      EntityColName = "CustomerID",
      ProductColName = "StockCode",
      MetricColName = "TotalSales"),
  WinningModel = AutoRecommender(
      AutoRecomDataCreate(
        data,
        EntityColName = "CustomerID",
        ProductColName = "StockCode",
        MetricColName = "TotalSales"),
      Partition = "Split",
      KFolds = 2,
      Ratio = 0.75,
      RatingType = "TopN",
      RatingsKeep = 20,
      SkipModels = "AssociationRules",
      ModelMetric = "TPR"),
  EntityColName = "CustomerID",
  ProductColName = "StockCode")
# }
# NOT RUN {
Results <- AutoRecommenderScoring(
  data = AutoRecomDataCreate(
      data,
      EntityColName = "CustomerID",
      ProductColName = "StockCode",
      MetricColName = "TotalSales"),
  WinningModel = AutoRecommender(
      AutoRecomDataCreate(
        data,
        EntityColName = "CustomerID",
        ProductColName = "StockCode",
        MetricColName = "TotalSales"),
      Partition = "Split",
      KFolds = 2,
      Ratio = 0.75,
      RatingType = "TopN",
      RatingsKeep = 20,
      SkipModels = "AssociationRules",
      ModelMetric = "TPR"),
  EntityColName = "CustomerID",
  ProductColName = "StockCode")
# }