Confussion Matrix

This function calculates a Confussion Matrix using crosstab for 2 or more categories. You can either set the score and threshold or the labels you wish to cross with.

Usage

conf_mat(tag, score, thresh = 0.5, sense = ">=", diagonal = TRUE, plot = FALSE)

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)

sense

Character. Inequation sense for threshold: <, <=, >=, >

diagonal

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

plot

Boolean. Plot result? Uses mplot_conf()

Value

data.frame. Result of counting tag and score's tag given a threshold, similar to base::table().

Details

You may use mplot_conf() or set plot=TRUE.

See also

Other Machine Learning: ROC(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), lasso_vars(), model_metrics(), model_preprocess(), msplit()

Other Model metrics: ROC(), errors(), gain_lift(), loglossBinary(), model_metrics()

Examples

data(dfr) # Results for AutoML Predictions
lapply(dfr[c(1, 2)], 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
#> 

# Results for Binomial Model
conf_mat(dfr$class2$tag, dfr$class2$scores)
#> # A tibble: 2 × 3
#>   `Real x Pred` `FALSE` `TRUE`
#>   <fct>           <int>  <int>
#> 1 FALSE               9    156
#> 2 TRUE               68     35
conf_mat(dfr$class2$tag, dfr$class2$scores, thresh = 0.3)
#> # A tibble: 2 × 3
#>   `Real x Pred` `FALSE` `TRUE`
#>   <fct>           <int>  <int>
#> 1 FALSE              27    138
#> 2 TRUE               84     19
conf_mat(dfr$class2$tag, dfr$class2$scores, sense = "<=")
#> # A tibble: 2 × 3
#>   `Real x Pred` `FALSE` `TRUE`
#>   <fct>           <int>  <int>
#> 1 FALSE             156      9
#> 2 TRUE               35     68

# Results for Multi-Categorical Model
conf_mat(dfr$class3$tag, dfr$class3$score)
#> # A tibble: 3 × 4
#>   `Real x Pred`   n_3   n_1   n_2
#>   <fct>         <int> <int> <int>
#> 1 n_3             120    11    18
#> 2 n_1              12    43     8
#> 3 n_2              26    15    15