Confusion Matrix for Categorical Data

Calculates a cross-tabulation of observed and predicted classes.

conf_mat(data, ...)

# S3 method for data.frame
conf_mat(data, truth, estimate, dnn = c("Prediction", "Truth"), ...)

# S3 method for conf_mat
tidy(x, ...)

autoplot.conf_mat(object, type = "mosaic", ...)

Arguments

data	A data frame or a `base::table()`.
...	Options to pass to `base::table()` (not including `dnn`). This argument is not currently used for the `tidy` method.
truth	The column identifier for the true class results (that is a `factor`). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For `_vec()` functions, a `factor` vector.
estimate	The column identifier for the predicted class results (that is also `factor`). As with `truth` this can be specified different ways but the primary method is to use an unquoted variable name. For `_vec()` functions, a `factor` vector.
dnn	A character vector of dimnames for the table.
x	A `conf_mat` object.
object	The `conf_mat` data frame returned from `conf_mat()`.
type	Type of plot desired, must be "mosaic" or "heatmap", defaults to "mosaic".

Value

conf_mat() produces an object with class conf_mat. This contains the table and other objects. tidy.conf_mat() generates a tibble with columns name (the cell identifier) and value (the cell count).

When used on a grouped data frame, conf_mat() returns a tibble containing columns for the groups along with conf_mat, a list-column where each element is a conf_mat object.

Details

For conf_mat() objects, a broom tidy() method has been created that collapses the cell counts by cell into a data frame for easy manipulation.

There is also a summary() method that computes various classification metrics at once. See summary.conf_mat()

There is a ggplot2::autoplot() method for quickly visualizing the matrix. Both a heatmap and mosaic type is implemented.

The function requires that the factors have exactly the same levels.

Examples

library(dplyr)
data("hpc_cv")

# The confusion matrix from a single assessment set (i.e. fold)
cm <- hpc_cv %>%
  filter(Resample == "Fold01") %>%
  conf_mat(obs, pred)
cm
#>           Truth
#> Prediction  VF   F   M   L
#>         VF 166  33   8   1
#>         F   11  71  24   7
#>         M    0   3   5   3
#>         L    0   1   4  10

# Now compute the average confusion matrix across all folds in
# terms of the proportion of the data contained in each cell.
# First get the raw cell counts per fold using the `tidy` method
library(purrr)
library(tidyr)

cells_per_resample <- hpc_cv %>%
  group_by(Resample) %>%
  conf_mat(obs, pred) %>%
  mutate(tidied = map(conf_mat, tidy)) %>%
  unnest(tidied)

# Get the totals per resample
counts_per_resample <- hpc_cv %>%
  group_by(Resample) %>%
  summarize(total = n()) %>%
  left_join(cells_per_resample, by = "Resample") %>%
  # Compute the proportions
  mutate(prop = value/total) %>%
  group_by(name) %>%
  # Average
  summarize(prop = mean(prop))

counts_per_resample
#> # A tibble: 16 x 2
#>    name         prop
#>    <chr>       <dbl>
#>  1 cell_1_1 0.467   
#>  2 cell_1_2 0.107   
#>  3 cell_1_3 0.0185  
#>  4 cell_1_4 0.00259 
#>  5 cell_2_1 0.0407  
#>  6 cell_2_2 0.187   
#>  7 cell_2_3 0.0632  
#>  8 cell_2_4 0.0173  
#>  9 cell_3_1 0.00173 
#> 10 cell_3_2 0.00692 
#> 11 cell_3_3 0.0228  
#> 12 cell_3_4 0.00807 
#> 13 cell_4_1 0.000575
#> 14 cell_4_2 0.0104  
#> 15 cell_4_3 0.0144  
#> 16 cell_4_4 0.0320  

# Now reshape these into a matrix
mean_cmat <- matrix(counts_per_resample$prop, byrow = TRUE, ncol = 4)
rownames(mean_cmat) <- levels(hpc_cv$obs)
colnames(mean_cmat) <- levels(hpc_cv$obs)

round(mean_cmat, 3)
#>       VF     F     M     L
#> VF 0.467 0.107 0.018 0.003
#> F  0.041 0.187 0.063 0.017
#> M  0.002 0.007 0.023 0.008
#> L  0.001 0.010 0.014 0.032

# The confusion matrix can quickly be visualized using autoplot()
library(ggplot2)

autoplot(cm, type = "mosaic")
autoplot(cm, type = "heatmap")

Arguments

Value

Details

See also

Examples