Let's understand the scatter plot and confusion matrix generated by Classification Learner App for k-fold cross-validation with an example of iris dataset having 150 samples and 5-fold cross-validation.
As we choose 5-folds, the app will partition the data into 5 disjoint sets or folds cross-validation. For each fold, the app trains a model using 4 folds as training data and remaining 1-fold (i.e. held-out fold) as validation data.
It means whenever we use k-fold cross-validation, all the 150 samples will be considered as validation data or held-out fold for once. For e.g., for first iteration 1st fold will be validation and remaining 4 folds will be training data and similarly for second iteration 2nd fold will be validation and remaining 4 folds will be training data.
Scatter plot: The each prediction shown in the scatter plot is obtained when that particular observation was a part of held-out fold or validation data while model was training.
Confusion Matrix: The confusion matrix depicts how correctly the model predicted the class of the observation when that particular observation was a part of held-out fold or validation data while model was training. Hence the values are integer in confusion matrix.
Accuracy: The accuracy is calculated for each k-fold and to calculate the accuracy for the model we do average.
Following are the scatter plot and confusion matrix which I got on iris data for 5-fold cross validation:
Hope it helps!