K-Fold Cross Validation - Intro to Machine Learning
Why Cross Validated Stack Exchange recommends it
Stats Stack Exchange answerers point people to it as a quick visual explanation of k-fold cross validation when someone is confused about training and testing on the same data.
Recommended by 2 people across 2 different months, 2017 to 2018.
What it is
A short Udacity clip from the Intro to Machine Learning course explaining k-fold cross validation: splitting data into several folds and rotating which fold is held out for testing. It is aimed at beginners learning how to evaluate models on unseen data.
What people said
Recommends J_Heads · 2017-05 · Cross Validated Stack Exchange · on “Confused about Cross validation”
I am currently doing cross validation on my decision trees using the whole data and then testing on the same data. What this is doing is evaluating the performance of your model on your training set. Your training error is not as important as the test error because you want to know how well your data will perform on unseen data. Cross validation is a technique used to estimate how well your model will perform on unseen data. Is the correct way to divide your data in training (~80%) (cross validation set) and test set (~20%) to later validate your model? Yes, this is common. This division result in: 60 % training 20 % validation 20 % testing. Cross validation involves creating several divisio
Recommends xboard · 2017-09 · Cross Validated Stack Exchange · on “Can a random forest be used to predict all data in a sample?”
The most straightforward answer is No ! You can't train and predict on the same data without biasing. Nevertheless you can use a technique called K-fold cross validation which involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are combined (e.g. averaged) over the rounds to estimate a final predictive model [LINK] . [LINK] You may also like to check this Udacity video [this video] on K-Fold Cross Vali
Cites SecretAgentMan · 2018-07 · Cross Validated Stack Exchange · on “How well does my logistic regression model fit?”
Have you considered cross validation (no pun intended) or a confusion matrix? Cross Validation [LINK] [this video] Confusion Matrix Confusion matrix ( [LINK] ) Other Resources Some Validation Links: [LINK] [LINK]