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Confussion Matrix Plot

Source: R/model_plots.R
mplot_conf.Rd

This function plots a confussion matrix.

Usage

mplot_conf(
  tag,
  score,
  thresh = 0.5,
  abc = TRUE,
  squared = FALSE,
  diagonal = TRUE,
  top = 20,
  subtitle = NA,
  model_name = NULL,
  save = FALSE,
  subdir = NA,
  file_name = "viz_conf_mat.png"
)

Arguments

tag

Vector. Real known label.

score

Vector. Predicted value or model's result.

thresh

Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)

abc

Boolean. Arrange columns and rows alphabetically?

squared

Boolean. Force plot to be squared?

diagonal

Boolean. FALSE to convert diagonal numbers to zeroes. Ideal to detect must confusing categories.

top

Integer. Plot only the most n frequent variables. Set to NA to plot all.

subtitle

Character. Subtitle to show in plot

model_name

Character. Model's name

save

Boolean. Save output plot into working directory

subdir

Character. Sub directory on which you wish to save the plot

file_name

Character. File name as you wish to save the plot

Value

Plot with confusion matrix results.

Details

You may use conf_mat() to get calculate values.

See also

Other ML Visualization: mplot_cuts(), mplot_cuts_error(), mplot_density(), mplot_full(), mplot_gain(), mplot_importance(), mplot_lineal(), mplot_metrics(), mplot_response(), mplot_roc(), mplot_splits(), mplot_topcats()

Examples

Sys.unsetenv("LARES_FONT") # Temporal
data(dfr) # Results for AutoML Predictions
lapply(dfr, head)
#> $class2
#>     tag    scores
#> 1  TRUE 0.3155498
#> 2  TRUE 0.8747599
#> 3  TRUE 0.8952823
#> 4 FALSE 0.0436517
#> 5  TRUE 0.2196593
#> 6 FALSE 0.2816101
#> 
#> $class3
#>   tag score        n_1        n_2        n_3
#> 1 n_3   n_2 0.20343865 0.60825062 0.18831071
#> 2 n_2   n_3 0.17856154 0.07657769 0.74486071
#> 3 n_1   n_1 0.50516951 0.40168718 0.09314334
#> 4 n_3   n_2 0.30880713 0.39062151 0.30057135
#> 5 n_2   n_3 0.01956827 0.07069011 0.90974158
#> 6 n_2   n_3 0.07830017 0.15408720 0.76761264
#> 
#> $regr
#>       tag    score
#> 1 11.1333 25.93200
#> 2 30.0708 39.91900
#> 3 26.5500 50.72246
#> 4 31.2750 47.81292
#> 5 13.0000 30.12853
#> 6 26.0000 13.24153
#> 

# Plot for Binomial Model
mplot_conf(dfr$class2$tag, dfr$class2$scores,
  model_name = "Titanic Survived Model"
)


# Plot for Multi-Categorical Model
mplot_conf(dfr$class3$tag, dfr$class3$score,
  model_name = "Titanic Class Model"
)