Accumulated local effects (Apley & Zhu 2020). A variable's range is cut into bins by its quantiles at the points; within each bin, every point is moved to the bin's lower edge and to its upper edge, and the difference of the two predictions is the local effect. Accumulated across the bins and centred, it is the curve. A point is moved only across its own bin, so the model is asked about values close to the ones it saw – which is why ALE is read with correlated predictors, where a partial dependence plot would ask about combinations that do not occur.
Arguments
- variables
Which variables, named as
importance_groups()names them (a channel, or a one-hot set). NULL: all of them. Groups of your own are not used: an effect needs one variable's values.- bins
Bins of a continuous variable's range, cut at its quantiles at the points – fewer where it has fewer distinct values.
Value
An importance_spec, for dsm_importance(). Its importance is the
spread of the curve over the points – the standard deviation of the
effect at their values; flat is no effect (Greenwell et al. 2018 for the
idea, on partial dependence).
Details
A point's whole patch is moved, every pixel and every window by the same amount: the neighbourhood keeps its texture and changes its level. Moving the centre pixel alone would build a spike no landscape has. A categorical – a one-hot set, or a binary map alone – has no range: its effect is the prediction with the whole patch made each class, less the prediction as it is. That is a partial dependence, which asks about a class also where it does not occur; the number of points in each class says where it does.