K-Fold Cross Validation

A way to validate the effectiveness of a machine learning model

$CV(\text{Model})=\frac{1}{K}\sum _{i=1}^{K}L({\hat{f}}_{C_{-i}}(C_i))$

• Lower value means better model
• $K$ is the number of chunks the data set is split into
• ${\hat{f}}_{C_{-i}}$ is the model fitted to the training set of all chunks that aren’t $C_i$, the validation set
• $L$ is the loss function