Google Interview Hacks

It's a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set. Examples: leave-one-out cross validation, K-fold cross validation. How to do it right? The training and validation data sets have to be drawn from the same population. Predicting stock prices: trained for a certain 5-year period, it's unrealistic to treat the subsequent 5-year a draw from the same population. Common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well. Bias-variance trade-off for k-fold cross ...

What is cross-validation? Cross-validation, sometimes called 'rotation estimation', is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset). The goal of cross validation is to define a dataset to "test" the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem), etc. How Cross-validation works? One round of cross-validation involves partiti...