Basic Examples 
(3)
 

Here is a dataset:
In[1]:=
dsTitanic=ResourceFunction["ExampleDataset"][{"MachineLearning","Titanic"}];​​dsTitanic〚100;;108;;2〛​​
Out[2]=
passenger class
passenger age
passenger sex
passenger survival
1st
48.0
female
survived
1st
39.0
male
died
1st
38.0
female
survived
1st
36.0
female
died
1st
—
female
survived
Here is a classification pipeline:
In[3]:=
clObj=​​
ClCon
[dsTitanic,<||>]⟹​​
ClConSplitData
[0.75]⟹​​
ClConEchoDataSummary
⟹​​
ClConMakeClassifier
["RandomForest"]⟹​​
ClConClassifierMeasurements
[{"Accuracy","Precision","Recall"}]⟹​​
ClConEchoValue
⟹​​
ClConROCPlot
;
»
summaries:trainingData
1 passenger class
3rd
534
1st
241
2nd
206
,
2 passenger age
Min
0.1667
1st Qu
21.
Median
28.
Mean
29.5625
3rd Qu
38.
Max
76.
Missing[___]
194
,
3 passenger sex
male
638
female
343
,
4 passenger survival
died
606
survived
375
,testData
1 passenger class
3rd
175
1st
82
2nd
71
,
2 passenger age
Min
0.3333
1st Qu
21.
Median
29.
Mean
30.8494
3rd Qu
40.
Max
80.
Missing[___]
69
,
3 passenger sex
male
205
female
123
,
4 passenger survival
died
203
survived
125

»
value:Accuracy0.783784,Precisiondied0.743316,survived0.888889,Recalldied0.945578,survived0.571429
»
ROC plot(s):died
,survived

The
ClCon
object is summarized with
MakeBoxes
:
In[4]:=
clObj
Out[4]=
ClCon
Training data dimensions: {981,4}
Test data dimensions: {328,4}
Classifier: ClassifierFunction
Input type: {Nominal,Numerical,Nominal}
Classes: died,survived
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Scope 
(3)
 
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