One-vs-One multiclass reduction (Geron Ch 3, morie.fn grovo)

K(K-1)/2 pairwise duels; a positive score votes for the SECOND class of the pair, ties in the vote count go to the lowest class index.

Usage

morie_geron_one_vs_one(X, y, base_fit = NULL, eta = 0.5, n_iter = 400)

Arguments

X: (m, n) design.
y: Integer class labels.
base_fit: Optional function(X, y_binary) returning a scorer.
eta, n_iter: Passed to morie_geron_train_logreg().

Value

List with predictions, votes, classes, pairs, n_classifiers, accuracy, ties.

Examples

X <- matrix(c(1, 2, 1, 3, 1, 5, 1, 7), 4, byrow = TRUE)
y <- c(2, 3, 6, 9)
th <- c(0.5, 1.1)
morie_geron_elastic_net_cost(X, y, th, 0.4, 0.6)
#> $cost
#> [1] 0.8033
#> 
#> $mse
#> [1] 0.4425
#> 
#> $l1_penalty
#> [1] 0.264
#> 
#> $l2_penalty
#> [1] 0.0968
#> 
#> $l1_norm
#> [1] 1.1
#> 
#> $l2_norm_sq
#> [1] 1.21
#> 
#> $alpha
#> [1] 0.4
#> 
#> $r
#> [1] 0.6
#> 
#> $estimate
#> [1] 0.8033
#> 
#> $n
#> [1] 4
#> 
morie_geron_one_vs_one(X = X, y = y)
#> $predictions
#> [1] 2 3 6 9
#> 
#> $votes
#>      [,1] [,2] [,3] [,4]
#> [1,]    3    2    1    0
#> [2,]    2    3    1    0
#> [3,]    0    1    3    2
#> [4,]    0    1    2    3
#> 
#> $classes
#> [1] 2 3 6 9
#> 
#> $pairs
#> $pairs[[1]]
#> [1] 2 3
#> 
#> $pairs[[2]]
#> [1] 2 6
#> 
#> $pairs[[3]]
#> [1] 2 9
#> 
#> $pairs[[4]]
#> [1] 3 6
#> 
#> $pairs[[5]]
#> [1] 3 9
#> 
#> $pairs[[6]]
#> [1] 6 9
#> 
#> 
#> $n_classifiers
#> [1] 6
#> 
#> $accuracy
#> [1] 1
#> 
#> $ties
#> [1] 0
#> 
#> $estimate
#> [1] 2 3 6 9
#> 
#> $n
#> [1] 4
#>