Compute decision tree from data set
compute_tree(
x,
data_test = NULL,
target_lab = NULL,
task = c("classification", "regression"),
feat_types = NULL,
label_map = NULL,
clust_samps = TRUE,
clust_target = TRUE,
custom_layout = NULL,
lev_fac = 1.3,
panel_space = 0.001
)Dataframe or a `party` or `partynode` object representing a custom tree. If a dataframe is supplied, conditional inference tree is computed. If a custom tree is supplied, it must follow the partykit syntax: https://cran.r-project.org/web/packages/partykit/vignettes/partykit.pdf
Tidy test dataset. Required if `x` is a `partynode` object. If NULL, heatmap displays (training) data `x`.
Name of the column in data that contains target/label information.
Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).
Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.
Named vector of the meaning of the target values, e.g., c(`0` = 'Edible', `1` = 'Poisonous').
Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.
Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.
Dataframe with 3 columns: id, x and y for manually input custom layout.
Relative weight of child node positions according to their levels, commonly ranges from 1 to 1.5. 1 for parent node perfectly in the middle of child nodes.
Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.
A list of results from `partykit::ctree` or provided custom tree, including fit, estimates, smart layout and terminal data.
fit_tree <- compute_tree(penguins, target_lab = 'species')
fit_tree$fit
#>
#> Model formula:
#> species ~ island + culmen_length_mm + culmen_depth_mm + flipper_length_mm +
#> body_mass_g + sex
#>
#> Fitted party:
#> [1] root
#> | [2] island in Torgersen, Dream
#> | | [3] culmen_length_mm <= 44.1
#> | | | [4] culmen_length_mm <= 42.3: Adelie (n = 100, err = 1.0%)
#> | | | [5] culmen_length_mm > 42.3: Adelie (n = 12, err = 41.7%)
#> | | [6] culmen_length_mm > 44.1: Chinstrap (n = 64, err = 3.1%)
#> | [7] island in Biscoe
#> | | [8] flipper_length_mm <= 203
#> | | | [9] culmen_length_mm <= 41.4: Adelie (n = 38, err = 0.0%)
#> | | | [10] culmen_length_mm > 41.4: Adelie (n = 7, err = 14.3%)
#> | | [11] flipper_length_mm > 203: Gentoo (n = 123, err = 0.0%)
#>
#> Number of inner nodes: 5
#> Number of terminal nodes: 6
fit_tree$layout
#> id x y
#> 1 1 0.5274183 1.0
#> 2 2 0.3289129 0.8
#> 3 3 0.2194596 0.6
#> 4 7 0.7259236 0.8
#> 5 8 0.6000531 0.6
#> 6 4 0.1400150 0.0
#> 7 5 0.2989042 0.0
#> 8 6 0.4131078 0.0
#> 9 9 0.5660389 0.0
#> 10 10 0.6340674 0.0
#> 11 11 0.8227470 0.0
dplyr::select(fit_tree$term_dat, - contains('nodedata'))
#> id parent birth_order breaks_label info info_list splitvar level kids
#> 1 4 3 1 NA <= NA.... NA c(0.8181.... <NA> 3 0
#> 2 5 3 2 NA > NA.... NA NA <NA> 3 0
#> 3 6 2 2 NA > NA.... NA c(63, 1..... <NA> 3 0
#> 4 9 8 1 NA <= NA.... NA NA <NA> 3 0
#> 5 10 8 2 NA > NA.... NA NA <NA> 3 0
#> 6 11 7 2 NA > NA.... NA NA <NA> 3 0
#> nodesize p.value horizontal x_parent y_parent y_hat n x y
#> 1 100 6.811624e-01 FALSE 0.1666667 0.6 Adelie 94 0.1400150 0
#> 2 12 NA FALSE 0.1666667 0.6 Adelie 12 0.2989042 0
#> 3 64 1.240239e-14 FALSE 0.2500000 0.8 Chinstrap 64 0.4131078 0
#> 4 38 NA FALSE 0.6666667 0.6 Adelie 38 0.5660389 0
#> 5 7 NA FALSE 0.6666667 0.6 Adelie 7 0.6340674 0
#> 6 123 NA FALSE 0.7500000 0.8 Gentoo 119 0.8227470 0
#> term_node
#> 1 Adelie
#> 2 Adelie
#> 3 Chinstrap
#> 4 Adelie
#> 5 Adelie
#> 6 Gentoo