Hi, I'm going through the book and the first thing I've encountered that seems a bit confusing is the part where we split model prediction for each outcome, tournament and pattern:
var itemInfo = {pattern: pattern, tournament: tournament, outcome: outcome}
var itemData = _.filter(towData, itemInfo)
var predicted_y = b0 + itemData[0]["nWins"]*b1
While itemData contains ~30 entries, with different participants ID, we take itemData[0]["nWins"] as a representative. I think that clarifying what pattern means would be beneficial for comprehension, because from the inference side everything is clear.
Hi, I'm going through the book and the first thing I've encountered that seems a bit confusing is the part where we split model prediction for each outcome, tournament and pattern:
While itemData contains ~30 entries, with different participants ID, we take
itemData[0]["nWins"]
as a representative. I think that clarifying what pattern means would be beneficial for comprehension, because from the inference side everything is clear.