Basic Examples (3)

Here is a dataset:

dsTitanic=ResourceFunction["ExampleDataset"][{"MachineLearning","Titanic"}];
dsTitanic[[100;;108;;2]]

Here is a classification pipeline:

clObj=
({{, ClCon }})[dsTitanic,<||>]⟹
({{, ClConSplitData }})[0.75]⟹
({{, ClConEchoDataSummary }})⟹
({{, ClConMakeClassifier }})["RandomForest"]⟹
({{, ClConClassifierMeasurements }})[{"Accuracy","Precision","Recall"}]⟹
({{, ClConEchoValue }})⟹
({{, ClConROCPlot }});

The ClCon object is summarized with MakeBoxes:

clObj

Scope (3)

Context (1)

The ClCon object is an association with the keys:

Keys[clObj⟹({{, ClConTakeContext }})]

Confusion matrix (1)

Here is the confusion matrix:

clObj⟹
({{, ClConClassifierMeasurements }})["ConfusionMatrixPlot"]⟹
({{, ClConTakeValue }})

Importance of variables (1)

Here is an importance of variables computation:

clObj⟹
({{, ClConAccuracyByVariableShuffling }})⟹
({{, ClConEchoValue }})⟹
({{, ClConEchoFunctionValue }})["normalized:",1-KeyDrop[#,None]/#[None]&];