After building Models(Paint those!)
Cross Validation: Each Sample is separated into random equal sized sub-samples, Helps to improve model performance.
Different Forms of cross Validation:
- Train-Test Split – low variance but more bias
- LOOCV(Leave one out Cross validation) – Leave one data point out and apply model on rest of the data. -low bias but high variance,
Now in the above two methods we have limitations related to Bias and variance, So what to do? Let’s fire-up ‘Cross-Validation’!
There are various other important Cross Validation Examples/Methods those are interesting like Time-series_Split, Leave_P_out(LPO), Random_permutation_Split(Shuffle and split), StarifiedKfold,:
Some classification problems can exhibit a large imbalance in the distribution of the target classes: for instance there could be several times more negative samples than positive samples. In such cases it is recommended to use stratified sampling as implemented in
StratifiedShuffleSplit to ensure that relative class frequencies is approximately preserved in each train and validation fold.