# prediction: Calculate Shapley weights for test data In shapr: Prediction Explanation with Dependence-Aware Shapley Values

## Description

This function should only be called internally, and not be used as a stand-alone function.

## Usage

 `1` ```prediction(dt, prediction_zero, explainer) ```

## Arguments

 `dt` data.table `prediction_zero` Numeric. The value to use for `phi_0`. `explainer` An object of class `explainer`. See `shapr`.

## Details

If `dt` does not contain three columns called `id`, `id_combination` and `w` the function will fail. `id` represents a unique key for a given test observation, and `id_combination` is a unique key for which feature combination the row represents. `w` represents the Shapley value of feature combination given by `id_combination`. In addition to these three columns, `dt` should also have columns which matches the variables used when training the model.

I.e. you have fitted a linear model using the features `x1`, `x2` and `x3`, and you want to explain 5 test observations using the exact method, i.e. setting `exact = TRUE` in `shapr`, the following properties should be satisfied

1. `colnames(dt)` equals `c("x1", "x2", "x3", "id", "id_combination", ""w)`

2. `dt[, max(id)]` equals the number of test observations

3. `dt[, min(id)]` equals 1L.

4. `dt[, max(id_combination)]` equals `2^m` where m equals the number of features.

5. `dt[, min(id_combination)]` equals 1L.

6. `dt[, type(w)]` equals `double`.

## Value

An object of class `c("shapr", "list")`. For more details see `explain`.

## Author(s)

Nikolai Sellereite

shapr documentation built on Jan. 28, 2021, 5:06 p.m.