Table of Contents
What if data is not IID?
Without the i.i.d. assumption (or exchangeability) the resampled datasets will not have a joint distribution similar to that of the original dataset. Any dependence structure has become “messed up” by the resampling.
Why does data need to be IID?
IID samples have the important property that the larger the sample becomes, the greater the probability the sample will closely resemble the population. A simple random sample of size n is any sample acquired in such a way that each subset of size n from the population has the same probability of being the sample.
Is cross validation always needed?
In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter.
What is non IID in federated learning?
However, models trained in federated learning usually have worse performance than those trained in the standard centralized learning mode, especially when the training data are not independent and identically distributed (Non-IID) on the local devices.
What does IID mean in statistics?
identically distributed
In statistics, we commonly deal with random samples. A random sample can be thought of as a set of objects that are chosen randomly. Or, more formally, it’s “a sequence of independent, identically distributed (IID) random variables”. In other words, the terms random sample and IID are basically one and the same.
What is IID and non IID data?
Literally, non iid should be the opposite of iid in either way, independent or identical . So for example, if a coin is flipped, let X is the random variable of event that result is tail, Y is the random variable of event the result is head, then X and Y are definitely dependent. They can be decided by each other.
What is the meaning of independently and identically distributed?
A collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent.
When should we use cross validation?
The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
What does cross validation tell us?
Cross-validation is a statistical method used to estimate the skill of machine learning models. That k-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset.
What is cross validation and why is it necessary?
Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.