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Difference between 2-fold and leave-one-out cross-validation?
2-fold is the simplest methode where the data is split into two equal parts.
Train on the first fold and validate on the second and vice versa.
Leave-one-out cross-validation splits the data into k equal parts where k is the number of samples in the training set.
Use a single sample as a validatoin set and all the rest as training set (k times).
Train on the first fold and validate on the second and vice versa.
Leave-one-out cross-validation splits the data into k equal parts where k is the number of samples in the training set.
Use a single sample as a validatoin set and all the rest as training set (k times).
Tags:
Quelle: CI Teil 1 Lecture 6
Quelle: CI Teil 1 Lecture 6
Karteninfo:
Autor: Sepp Samuel
Oberthema: Telematik
Thema: Computational Intelligence
Schule / Uni: TU Graz
Veröffentlicht: 02.07.2014