ALE plots: How does argument `grid.size` effect the results?

Why is the length of the resulting DF per feature so different when setting `grid.size = 99`?

I was not able to relate the setting to the actual outcome differences by reading `?FeatureEffect`.

``` r
library(iml)
library(rpart)

data("Boston", package = "MASS")
rf = rpart(medv ~ ., data = Boston)
mod = Predictor$new(rf, data = Boston)

# Compute the accumulated local effects for all features
eff = FeatureEffects$new(mod, grid.size = 99)

purrr::map_int(eff$results, nrow)
#> crim zn indus chas nox rm age dis rad tax
#> 100 20 50 2 62 100 92 100 9 45
#> ptratio black lstat
#> 37 77 100
```

^{Created on 2020-01-15 by the [reprex package](https://reprex.tidyverse.org) (v0.3.0.9001)}

Edit: The following code sets the grid

https://github.com/christophM/iml/blob/54b2ce26d8d13f9a6fcd635ee00c8d4835b2cad3/R/FeatureEffect-ale.R#L17-L17

and in more detail this one

https://github.com/christophM/iml/blob/54b2ce26d8d13f9a6fcd635ee00c8d4835b2cad3/R/utils.R#L191-L192

So essentially `quantile(type = 1)` is called with `probs` being a seq with length.out set by `grid.size`.

I wonder if this could make it into the argument description in the help page?
Maybe one could also include the motivation for `type = 1`.

The reason for the differing outcomes shown above is then caused by

https://github.com/christophM/iml/blob/54b2ce26d8d13f9a6fcd635ee00c8d4835b2cad3/R/FeatureEffect-ale.R#L16-L17

which removes duplicated values from the `quantile()` output.

Regarding interpretation: Does the differing number of unique values for these features introduce a bias when interpreting the ALE plots for the specific predictors?
Or is it like "20 is fine, everything greater is better but there is no bias when comparing the ALE plots of these features.".