Cross-Validation

How good are supervised data mining methods on your classification data set? Here's a workflow that scores various classification techniques on a data set from medicine. The central widget here is the one for testing and scoring, which is given the data and a set of learners, does cross-validation and scores predictive accuracy, and outputs the scores for further examination.

File

Test & Score

Logistic Regression

Random Forest Classification

SVM

Confusion Matrix

Data Table

Data Table (1)

Choose class-labeled data set. Say, "iris.tab" from documentation data sets.
It's always a good idea to check out the data first.
Select a cell in confusion matrix to obtain related data instances. Here we examine them in the spreadheet.
Use for additional analysis of cross-validation results.
Cross-validation takes place here. Double click to see the performance scores.
Several learners can be scored in cross-validation at the same time.
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