How do you calculate test error and training error?
Remember that the training error is calculated by using the same data for training the model and calculating its error rate. For calculating the test error, you are using completely disjoint data sets for both tasks.
How do you calculate cross validation error?
1 Answer
- The most common way of calculating overall error for cross validation is to pool the predictions of all folds and then calculate the error of the pooled predictions.
- The answer you link instead pools the errors obtained for the individual surrogate models.
How is a training error different from a test error?
It is very important to understand the difference between a training error and a test error. Remember that the training error is calculated by using the same data for training the model and calculating its error rate. For calculating the test error, you are using completely disjoint data sets for both tasks.
How to estimate the training-sample classification error?
Alternatively, if Mdl is not compact, then you can estimate the training-sample classification error by passing Mdl to resubLoss. Classification loss functions measure the predictive inaccuracy of classification models. When you compare the same type of loss among many models, a lower loss indicates a better predictive model.
How is performance of estimator on unseen data not the same as training data?
Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases the performance on train decreases while the performance on test is optimal within a range of values of the regularization parameter.
How to prevent model errors in machine learning?
Since the consequences are often dire, I’m going to discuss how to prevent mistakes in model validation and the necessary components of a correct validation. To kick off the discussion, let’s get grounded in some of the basic concepts of validating machine learning models: predictive modeling, training error, test error and cross validation