Description Usage Arguments Value Examples
Given a list of models, get their predictions over a range of
values from a specified feature. This is done by copying an input matrix X
several times and replacing a specified feature with different values for each copy.
The list of models then make predictions on this modified data.
1 2 3 4 | calculate_ice(feature_dt, feature_col, model_list, num_grid = 10,
custom_range = NULL, predict_fcn = predict,
ensemble_colname = "ensemble", ensemble_fcn = median,
ensemble_models = names(model_list))
|
feature_dt |
data.table containing features used in predictive model |
feature_col |
character. name of a column in |
model_list |
named list of model objects. Each name will become a column containing predictions from that model. |
num_grid |
number of points to distribute along range of
|
custom_range |
should only be used if |
predict_fcn |
function that accepts a model as its first argument
and |
ensemble_colname |
character. Name of the column containing ensemble predictions |
ensemble_fcn |
function that combines a vector of predictions into
a single ensemble. Default is |
ensemble_models |
character vector of names from model_list. These models will be combined by ensemble_fcn to form the ensemble |
Output is a data.table
of the same structure as feature_dt
,
with several changes:
feature_dt
is copied num_grid
or length(custom_range)
times
the values in feature_col
will have been modified
new columns will have been added. One for each value in model_list
as well as ensemble_colname
an id
column will have been added to track observations
1 2 3 4 5 6 7 8 9 10 11 12 | ## Not run:
dt <- data.table(a = 1:3, b = 2:4, c = c(8, 11, 14))
m <- lm(c ~ a + b - 1, dt)
gm <- glm(c ~ a + b - 1, data = dt)
calculate_ice(dt, "a", list(lm1 = m),
num_grid = 6)
calculate_ice(dt, "a", list(lm1 = m, glm1 = gm),
num_grid = 6, ensemble_fcn = sum)
calculate_ice(dt, "a", list(lm1 = m),
num_grid = 6, custom_range = c(1,6))
## End(Not run)
|
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