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Predictive quality metrics for a node of a COVLMC context tree

Source: R/ctx_node_covlmc.R
metrics.ctx_node_covlmc.Rd

This function computes and returns predictive quality metrics for a node (ctx_node_covlmc) extracted from a covlmc

Usage

# S3 method for class 'ctx_node_covlmc'
metrics(model, ...)

Arguments

model

A ctx_node_covlmc object as returned by find_sequence() or contexts.covlmc()

...

Additional parameters for predictive metrics computation.

Value

an object of class metrics.covlmc with the following components:

  • accuracy: the accuracy of the predictions

  • conf_mat: the confusion matrix of the predictions, with predicted values in rows and true values in columns

  • auc: the AUC of the predictive model

Details

Compared to metrics.covlmc(), this function focuses on a single context and assesses the quality of its predictions, disregarding observations that have other contexts. Apart from this limited scope, the function operates as metrics.covlmc().

References

David J. Hand and Robert J. Till (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems." Machine Learning 45(2), p. 171–186. DOI: doi:10.1023/A:1010920819831 .

See also

metrics.vlmc(), metrics.ctx_node(), contexts.vlmc(), predict.vlmc().

Examples

pc <- powerconsumption[powerconsumption$week == 5, ]
breaks <- c(
  0,
  median(powerconsumption$active_power, na.rm = TRUE),
  max(powerconsumption$active_power, na.rm = TRUE)
)
labels <- c(0, 1)
dts <- cut(pc$active_power, breaks = breaks, labels = labels)
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
m_cov <- covlmc(dts, dts_cov, min_size = 5)
m_ctxs <- contexts(m_cov)
## get the predictive metrics for each context
lapply(m_ctxs, metrics)
#> [[1]]
#> Context [T]: 0 
#>  followed by 0 (318), 1 (31)
#>  Confusion matrix: 
#>     0   1  
#>   0 318 31 
#>   1 0   0  
#>  Accuracy: 0.9112 
#>  AUC: 0.6111 
#> 
#> [[2]]
#> Context [T]: 0, 1 
#>  followed by 0 (4), 1 (27)
#>  Confusion matrix: 
#>     0 1  
#>   0 0 0  
#>   1 4 27 
#>  Accuracy: 0.871 
#>  AUC: 0.5 
#> 
#> [[3]]
#> Context [T]: 0, 1, 1 
#>  followed by 0 (5), 1 (22)
#>  Confusion matrix: 
#>     0 1  
#>   0 0 0  
#>   1 5 22 
#>  Accuracy: 0.8148 
#>  AUC: 0.5 
#> 
#> [[4]]
#> Context [T]: 1, 1, 1 
#>  followed by 0 (23), 1 (575)
#>  Confusion matrix: 
#>     0  1   
#>   0 0  0   
#>   1 23 575 
#>  Accuracy: 0.9615 
#>  AUC: 0.5 
#>