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iai: Interpretable AI R Interface

iai is a package providing an interface to the algorithms of Interpretable AI from the R programming language, including:

  • Optimal Trees for classification, regression, prescription and survival analysis
  • Optimal Imputation for missing data imputation and outlier detection
  • Optimal Feature Selection for exact sparse regression

Installation and Usage

Please refer to the official Interpretable AI documentation for information on setting up and using the package.

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Version

Install

install.packages('iai')

Monthly Downloads

400

Version

1.0.0

License

MIT + file LICENSE

Maintainer

Jack Dunn

Last Published

July 18th, 2019

Functions in iai (1.0.0)

apply

apply
optimal_tree_prescription_minimizer

optimal_tree_prescription_minimizer
get_num_nodes

get_num_nodes
get_split_threshold

get_split_threshold
optimal_tree_prescription_maximizer

optimal_tree_prescription_maximizer
get_split_feature

get_split_feature
get_lower_child

get_lower_child
get_grid_results

get_grid_results
write_questionnaire

write_questionnaire
get_classification_label

get_classification_label
get_regression_weights

get_regression_weights
get_best_params

get_best_params
get_regression_constant

get_regression_constant
get_depth

get_depth
get_classification_proba

get_classification_proba
get_learner

get_learner
grid_search

grid_search
print_path

print_path
predict_proba

predict_proba
iai_setup

iai_setup
score

score
missing_goes_lower

missing_goes_lower
mean_imputation_learner

mean_imputation_learner
get_num_samples

get_num_samples
apply_nodes

apply_nodes
get_params

get_params
set_display_label

set_display_label
is_categoric_split

is_categoric_split
impute_cv

impute_cv
get_parent

get_parent
get_prescription_treatment_rank

get_prescription_treatment_rank
get_survival_curve_data

get_survival_curve_data
transform

transform
variable_importance

variable_importance
get_upper_child

get_upper_child
optimal_tree_regressor

optimal_tree_regressor
optimal_tree_classifier

optimal_tree_classifier
opt_tree_imputation_learner

opt_tree_imputation_learner
set_params

set_params
is_parallel_split

is_parallel_split
rand_imputation_learner

rand_imputation_learner
read_json

read_json
set_julia_seed

set_julia_seed
write_dot

write_dot
is_ordinal_split

is_ordinal_split
optimal_tree_survivor

optimal_tree_survivor
get_rich_output_params

get_rich_output_params
write_html

write_html
roc_curve

roc_curve
write_png

write_png
reset_display_label

reset_display_label
write_json

write_json
get_split_categories

get_split_categories
is_leaf

is_leaf
is_mixed_ordinal_split

is_mixed_ordinal_split
is_hyperplane_split

is_hyperplane_split
is_mixed_parallel_split

is_mixed_parallel_split
predict

predict
predict_outcomes

predict_outcomes
single_knn_imputation_learner

single_knn_imputation_learner
set_threshold

set_threshold
set_rich_output_param

set_rich_output_param
split_data

split_data
get_survival_curve

get_survival_curve
imputation_learner

imputation_learner
get_split_weights

get_split_weights
impute

impute
opt_knn_imputation_learner

opt_knn_imputation_learner
opt_svm_imputation_learner

opt_svm_imputation_learner
show_questionnaire

show_questionnaire
show_in_browser

show_in_browser
fit_cv

fit_cv
delete_rich_output_param

delete_rich_output_param
decision_path

decision_path
fit

fit
clone

clone
as.mixeddata

as.mixeddata
fit_transform

fit_transform
fit_transform_cv

fit_transform_cv