Interface to 'Interpretable AI' Modules
Download and install the IAI system image automatically.
Check if a node of a tree applies a categoric split
Check if a node of a tree applies a hyperplane split
Check if a node of a tree is a leaf
Check if a node of a tree applies a mixed ordinal/categoric split
Check if a node of a tree applies a mixed parallel/categoric split
Check if a node of a tree applies a ordinal split
Check if a node of a tree applies a parallel split
Loads the Julia Graphviz library to permit certain visualizations.
Learner for conducting mean imputation
Check if points with missing values go to the lower child at a split n...
Construct an interactive questionnaire from multiple specified learner...
Construct an interactive tree questionnaire using multiple learners fr...
Generic function for constructing an interactive questionnaire with mu...
Construct an interactive tree visualization of multiple tree learners ...
Construct an interactive tree visualization of multiple tree learners ...
Generic function for constructing an interactive tree visualization of...
Learner for conducting reward estimation with numeric treatments and c...
Learner for conducting reward estimation with numeric treatments and r...
Learner for conducting reward estimation with numeric treatments
Learner for conducting reward estimation with numeric treatments and s...
Learner for conducting optimal k-NN imputation
Learner for conducting optimal SVM imputation
Learner for conducting optimal tree-based imputation
Learner for conducting Optimal Feature Selection on classification pro...
Learner for conducting Optimal Feature Selection on regression problem...
Learner for training Optimal Classification Trees
Learner for training multi-task Optimal Classification Trees
Learner for training multi-task Optimal Regression Trees
Learner for training Optimal Policy Trees where the policy should aim ...
Learner for training Optimal Policy Trees where the policy should aim ...
Learner for training Optimal Prescriptive Trees where the prescription...
Learner for training Optimal Prescriptive Trees where the prescription...
Learner for training Optimal Regression Trees
Learner for training Optimal Survival Trees
Learner for training Optimal Survival Trees
Plot a grid search results for Optimal Feature Selection learners
Plot an ROC curve
Plot a similarity comparison
Plot a stability analysis
Return the expected survival time estimate made by a `glmnetcv_surviva...
Generic function for returning the expected survival time predicted by...
Return the expected survival time estimate made by a survival curve (a...
Return the expected survival time estimate made by a survival learner ...
Return the fitted hazard coefficient estimate made by a `glmnetcv_surv...
Generic function for returning the hazard coefficient predicted by a m...
Return the fitted hazard coefficient estimate made by a survival learn...
Return a dataframe containing all treatment combinations of one or mor...
Return the indices of the points in the features that fall into each n...
Return the leaf index in a tree model into which each point in the fea...
Convert a vector of values to IAI mixed data format
Construct a [list("ggplot2::ggplot")](https://ggplot2.tidyverse.org/re...
Construct a [list("ggplot2::ggplot")](https://ggplot2.tidyverse.org/re...
Construct a [list("ggplot2::ggplot")](https://ggplot2.tidyverse.org/re...
Construct a [list("ggplot2::ggplot")](https://ggplot2.tidyverse.org/re...
Learner for conducting reward estimation with categorical treatments a...
Learner for conducting reward estimation with categorical treatments a...
Learner for conducting reward estimation with categorical treatments
Learner for conducting reward estimation with categorical treatments a...
Remove all traces of automatic Julia/IAI installation
Return an unfitted copy of a learner with the same parameters
Convert treatments from symbol/string format into numeric values.
Copy the tree split structure from one learner into another and refit ...
Return a matrix where entry (i, j) is true if the ith point in the...
Delete a global rich output parameter
Learner that estimates equal propensity for all treatments.
Fit an imputation learner with training features and create adaptive i...
Fits a grid search to the training data with cross-validation
Fits a model to the training data
Fits an Optimal Feature Selection learner to the training data
Generic function for fitting a learner.
Return the best parameter combination from a grid
Return the predicted label at a node of a tree
Return the total kernel density surrounding each treatment candidate f...
Return the names of the features used by the learner
Return a vector of lists detailing the results of the grid search
Return a summary of the results from the grid search
Return a summary of the results from the grid search
Return the fitted learner using the best parameter combination from a ...
Get the index of the lower child at a split node of a tree
Return the machine ID for the current computer.
Return the number of fits along the path in a trained GLMNet learner
Return the number of fits along the path in a trained Optimal Feature ...
Generic function for returning the number of fits in a trained learner
Return the number of nodes in a trained learner
Get the number of training points contained in a node of a tree
Return the value of all parameters on a learner
Get the index of the parent node at a node of a tree
Return the standard error for the quality of the treatments at a node ...
Return the quality of the treatments at a node of a tree
Return the treatments ordered from most effective to least effective a...
Return the constant term in the prediction in a trained GLMNet learner
Return the constant term in the prediction in a trained Optimal Featur...
Generic function for returning the prediction constant in a trained le...
Return the weights for numeric and categoric features used for predict...
Return the weights for numeric and categoric features used for predict...
Generic function for returning the prediction weights in a trained lea...
Return the treatments ordered from most effective to least effective a...
Return the constant term in the logistic regression prediction at a no...
Return the weights for each feature in the logistic regression predict...
Return the categoric/ordinal information used in the split at a node o...
Return the feature used in the split at a node of a tree
Return the threshold used in the split at a node of a tree
Return the weights for numeric and categoric features used in the hype...
Return the trained trees in order of increasing objective value, along...
Extract the underlying data from a survival curve (as returned by `pre...
Return the survival curve at a node of a tree
Return the predicted expected survival time at a node of a tree
Return the predicted hazard ratio at a node of a tree
Extract the training objective value for each candidate tree in the co...
Return a copy of the learner that uses a specific tree rather than the...
Get the index of the upper child at a split node of a tree
Learner for training GLMNet models for classification problems with cr...
Learner for training GLMNet models for regression problems with cross-...
Learner for training GLMNet models for survival problems with cross-va...
Controls grid search over parameter combinations
Initialize Julia and the IAI package.
Generic learner for imputing missing values
Impute missing values using cross validation
Impute missing values using either a specified method or through valid...
Download and install Julia automatically.
Return the predictions made by a supervised learner for each point in ...
Learner for training random forests for classification problems
Learner for training random forests for regression problems
Learner for training random forests for survival problems
Read in a learner or grid saved in JSON format
Refit the models in the leaves of a trained learner using the supplied...
Release any IAI license held by the current session.
Reset the predicted probability displayed to be that of the predicted ...
Resume training from a checkpoint file
Learner for conducting reward estimation with categorical treatments
Construct an ROC curve using a trained classification learner on the g...
Calculate the scores for a numeric reward estimator on the given data
Calculate the score for an Optimal Feature Selection learner on the gi...
Generic function for calculating scores
Calculate the score for a model on the given data
Show interactive visualization of a roc_curve in the default browser
Show interactive tree visualization of a tree learner in the default b...
Show an interactive questionnaire based on an Optimal Feature Selectio...
Generic function for showing interactive questionnaire in browser
Show an interactive questionnaire based on a tree learner in default b...
Conduct a similarity comparison between the final tree in a learner an...
Learner for conducting heuristic k-NN imputation
Split the data into training and test datasets
Return the predicted outcome for each treatment made by a policy learn...
Conduct a stability analysis of the trees in a tree learner
Transform features with a trained imputation learner and create adapti...
Impute missing values in a dataframe using a fitted imputation model
Specify an interactive tree visualization of a tree learner
Return the predicted outcome for each treatment made by a prescription...
Generic function for returning the outcomes predicted by a model under...
Conduct the reward kernel bandwidth tuning procedure for a range of st...
Return the probabilities of class membership predicted by a classifica...
Return the standard error for the estimated quality of each treatment ...
Return the estimated quality of each treatment in the trained model of...
Return the treatments in ranked order of effectiveness for each point ...
Calculate similarity between the final tree in a tree learner with all...
Generate a ranking of the variables in a learner according to their im...
Return counterfactual rewards estimated by a categorical reward estima...
Return the predictions made by a GLMNet learner for each point in the ...
Return counterfactual rewards estimated by a numeric reward estimator ...
Return the predictions made by an Optimal Feature Selection learner fo...
Generic function for returning the predictions of a model
Generate a ranking of the variables in an Optimal Feature Selection le...
Generic function for calculating variable importance
Generate a ranking of the variables in a tree learner according to the...
Write the internal booster saved in the learner to file
Output a learner in [.dot format](https://www.graphviz.org/content/dot...
Output an object as an interactive browser visualization in HTML forma...
Generic function for writing interactive visualization to file
Output an ROC curve as an interactive browser visualization in HTML fo...
Output a tree learner as an interactive browser visualization in HTML ...
Output a learner or grid in JSON format
Output a learner as a PDF image
Output a learner as a PNG image
Output an Optimal Feature Selection learner as an interactive question...
Generic function for writing interactive questionnaire to file
Output a tree learner as an interactive questionnaire in HTML format
Output a learner as a SVG image
Learner for training XGBoost models for classification problems
Learner for training XGBoost models for regression problems
Learner for training XGBoost models for survival problems
Learner for conducting zero-imputation
Acquire an IAI license for the current session.
Add additional Julia worker processes to parallelize workloads
Fit a categorical reward estimator on features, treatments and outcome...
Fit a numeric reward estimator on features, treatments and outcomes an...
Generic function for fitting a reward estimator on features, treatment...
Train a grid using cross-validation with features and impute all missi...
Fit an imputation model using the given features and impute the missin...
Fits a grid_search to the training data
Fits an imputation learner to the training data.
Get the depth of a node of a tree
Return the predicted label at a node of a multi-task tree
Generic function for returning the predicted label in the node of a cl...
Return the predicted probabilities of class membership at a node of a ...
Return the predicted probabilities of class membership at a node of a ...
Generic function for returning the probabilities of class membership a...
Return the indices of the trees assigned to each cluster, under the cl...
Return the centroid information for each cluster, under the clustering...
Return the distances between the centroids of each pair of clusters, u...
Return the constant term in the logistic regression prediction at a no...
Return the constant term in the linear regression prediction at a node...
Generic function for returning the constant term in the regression pre...
Return the constant term in the linear regression prediction at a node...
Return the constant term in the linear regression prediction at a node...
Return the constant term in the cox regression prediction at a node of...
Extract the underlying data from an ROC curve
Return the weights for each feature in the logistic regression predict...
Return the weights for each feature in the linear regression predictio...
Generic function for returning the weights for each feature in the reg...
Return the weights for each feature in the linear regression predictio...
Return the weights for each feature in the linear regression predictio...
Return the weights for each feature in the cox regression prediction a...
Return the current global rich output parameter settings
Return the probabilities of class membership predicted by a multi-task...
Return the probabilities of class membership predicted by a `glmnetcv_...
Generic function for returning the probabilities of class membership p...
Return counterfactual rewards estimated by a categorical reward estima...
Return counterfactual rewards estimated by a numeric reward estimator ...
Generic function for returning the counterfactual rewards estimated by...
Calculate SHAP values for all points in the features using the learner
Return the predictions made by a multi-task supervised learner for eac...
Return the predictions made by a survival learner for each point in th...
Print the decision path through the learner for each sample in the fea...
Use the trained trees in a learner along with the supplied validation ...
Specify an interactive questionnaire of an Optimal Feature Selection l...
Generic function for constructing an interactive questionnaire
Specify an interactive questionnaire of a tree learner
Learner for conducting random imputation
Construct an ROC curve using a trained multi-task classification learn...
Construct an ROC curve from predicted probabilities and true labels
Construct an ROC curve using a trained glmnetcv_classifieron the giv...
Generic function for constructing an ROC curve
Calculate the scores for a categorical reward estimator on the given d...
Calculate the score for a set of predictions on the given data
Calculate the score for a GLMNet learner on the given data
Calculate the score for a multi-task model on the given data
Show the probability of a specified label when visualizing a learner
Set the random seed in Julia
Set all supplied parameters on a learner
Save a new reward kernel bandwidth inside a learner, and return new re...
Sets a global rich output parameter
For a binary classification problem, update the the predicted labels i...
Show interactive visualization of an object in the default browser
Generic function for showing interactive visualization in browser
An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.