AI News, BOOK REVIEW: The main trick in Machine Learning

The main trick in Machine Learning

I have been irritated that many recent introductions to machine learning/neural networks/whatever that fail to emphasise the most import trick in machine learning.

Many internet resources don’t mention it, and even good textbooks often don’t drill it in to the reader the absolute criticality to success the trick is.

There is no easy formula to predict the ability of a learning system to generalise, but you can estimate it using held out data.

With a validation set in hand, you ask a learning system to make predictions on data you already know the answers to.

Vladimir Vapnik (the V in VC dimension), somewhat sarcastically describes the mindset of the 1970s applied learning community in the following excerpt from “The Nature of Statistical Learning Theory”

The principle of minimizing the number of training errors is a self-inductive principle, and from the practical point of view does not need justification.

In an iterative training procedure like neural network back propagation, the parameters of the learning model are fiddled with to reduce training error.

If you plot the training error, and validation error, against the number of training iterations you get the most important graph in machine learning:

Cross Validation

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