Print Method for tidy_apriori
Usage
# S3 method for class 'tidy_apriori'
print(x, ...)Examples
# \donttest{
if (requireNamespace("arules", quietly = TRUE)) {
data("Groceries", package = "arules")
res <- tidy_apriori(Groceries, support = 0.001, confidence = 0.5)
print(res)
}
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.5 0.1 1 none FALSE TRUE 5 0.001 2
#> maxlen target ext
#> 10 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 9
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
#> sorting and recoding items ... [157 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 done [0.01s].
#> writing ... [5668 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
#> Tidy Apriori Results
#> ====================
#>
#> Parameters:
#> Minimum support: 0.001
#> Minimum confidence: 0.5
#> Rule length: 2 - 10
#>
#> Results:
#> Number of rules: 5668
#>
#> Quality Measure Summary:
#> Support: 0.0010 - 0.0223 (mean: 0.0017)
#> Confidence: 0.5000 - 1.0000 (mean: 0.6250)
#> Lift: 1.96 - 19.00 (mean: 3.26)
#>
#> Top 5 rules by lift:
#> # A tibble: 5 × 8
#> rule_id lhs rhs support confidence coverage lift count
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 53 {Instant food products,… {ham… 0.00122 0.632 0.00193 19.0 12
#> 2 37 {soda,popcorn} {sal… 0.00122 0.632 0.00193 16.7 12
#> 3 444 {flour,baking powder} {sug… 0.00102 0.556 0.00183 16.4 10
#> 4 327 {ham,processed cheese} {whi… 0.00193 0.633 0.00305 15.0 19
#> 5 55 {whole milk,Instant foo… {ham… 0.00153 0.5 0.00305 15.0 15
#>
#> Use inspect_rules() to view more rules
#> Use visualize_rules() to create visualizations
# }