Machine Learning: The Art and Science of Algorithms that by Peter Flach

By Peter Flach

As the most accomplished computing device studying texts round, this e-book does justice to the field's fantastic richness, yet with no wasting sight of the unifying rules. Peter Flach's transparent, example-based strategy starts off by way of discussing how a unsolicited mail filter out works, which provides a right away creation to laptop studying in motion, with no less than technical fuss. Flach offers case reports of accelerating complexity and diversity with well-chosen examples and illustrations all through. He covers quite a lot of logical, geometric and statistical types and cutting-edge subject matters comparable to matrix factorisation and ROC research. specific realization is paid to the valuable position performed via beneficial properties. using tested terminology is balanced with the advent of latest and beneficial recommendations, and summaries of suitable historical past fabric are supplied with tips for revision if important. those good points determine laptop studying will set a brand new general as an introductory textbook.

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We will call this predictive clustering to distinguish it from the previous, descriptive form of clustering. Although we will not cover it in this book, it is worth pointing out a fifth setting of semisupervised learning of predictive models. In many problem domains data is cheap, but labelled data is expensive. For example, in web page classification you have the whole world-wide web at your disposal, but constructing a labelled training set is a painstaking process. One possible approach in semi-supervised learning is to use a small labelled training set to build an initial model, which is then refined using the unlabelled data.

There are a number of options to deal with overlapping rules, which will be further considered in Chapter 6. Tree-learning algorithms typically work in a top–down fashion. The first task is to find a good feature to split on at the top of the tree. The aim here is to find splits that result in improved purity of the nodes on the next level, where the purity of a node refers to the degree in which the training examples belonging to that node are of the same class. Once the algorithm has found such a feature, the training set is partitioned into subsets, one for each node resulting from the split.

If all number sequences are equally likely, then there is no hope that we can improve – on average – on random guessing (I personally always answer ‘42’ to such questions). Of course, some sequences are very much more likely than others, at least in the world of psychological tests. Likewise, the distribution of learning problems in the real world is highly non-uniform. The way to escape the curse of the no free lunch theorem is to find out more about this distribution and exploit this knowledge in our choice of learning algorithm.

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