Python Machine Learning Cookbook - Second Edition

How it works...

HMMs assume that the current output is statistically independent of the previous outputs. This is needed by HMMs to ensure that the inference works in a robust way. However, this assumption doesn't always have to be true! The current output in a time series setup, more often than not, depends on previous outputs. One of the main advantages of CRFs over HMMs is that they are conditional by nature, which means that we are not assuming any independence between output observations. There are a few other advantages of using CRFs over HMMs. CRFs tend to outperform HMMs in a number of applications, such as linguistics, bioinformatics, speech analysis, and so on. In this recipe, we will learn how to use CRFs to analyze sequences of ...