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Extra info for Unsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis
Example text
Since pi(k) is a probability, there is a constraint that all the pi’s have to sum to 1. Another way of thinking of this is that we introduced a new latent variable called “z”. “Z” represents which Gaussian the data came from. So we can say pi(k) = P(z = k). It’s like saying there’s some hidden cause called “Z” that we don’t know about and can’t measure, but that each of these “Z”s is causing a Gaussian to be generated, and all we can see in our data is the combined effects of those individual “Z”s.
This will help you not only appreciate the similarities between the two, but also to understand the limitations of K-means and why they exist. So let’s compare the 2 steps of each training algorithm. You see that the first step in both is to calculate the responsibilities, and the second step in both is to calculate the model parameters. K]{ pi(j)N(x(n), mu(j), C(j)) } The first thing is we now understand why K-Means looks for clusters of equal weight. It’s because it has no “pi” variable, which is equivalent to saying pi is uniform, or equal to 1/K.
All of the materials required to follow along in this book are free: You just need to able to download and install Python, Numpy, Scipy, Matplotlib, and Sci-kit Learn. Chapter 1: What is unsupervised learning used for? In general: unsupervised learning is for learning the structure or the probability distribution of the data. What does this mean specifically? In this chapter we’ll talk about some specific examples of how you can use unsupervised learning in your data pipeline. Density Estimation: You already know that we use the PDF, or probability density function, to tell us the probability of a random variable.