Hands-On Transfer Learning with Python

Bias variance trade-off

Supervised learning algorithms help us infer or learn a mapping from input data points to output signals. This learning results in a target or a learned function. Now, in an ideal scenario, the target function would learn the exact mapping between input and output variables. Unfortunately, there are no ideals.

As discussed while introducing supervised learning algorithms, we utilized a subset of data called the training dataset to learn the target function and then test the performance on another subset called the test dataset. Since the algorithm only sees a subset of all possible combinations of data, there arises an error between the predicted outputs and the observed outputs. This is called the total error or the ...