Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

Decision Tree regression

Decision Trees can also be employed in order to solve regression problems. However, in this case, it's necessary to consider a slightly different way of splitting the nodes. Instead of considering an impurity measure, one of the most common choices is to pick the feature that minimizes the mean squared error (MSE), considering the average prediction of a node. Let's suppose that a node, i, contains m samples. The average prediction is as follows:

At this point, the algorithm has to look for all of the binary splits in order to find the one that minimizes the target function:

Analogous to classification trees, the procedure is repeated until the MSE is below a fixed threshold, λ. Even if it's not correct, we can think about an unacceptable impurity level when the prediction of a node has a low accuracy. In fact, in a classification...