Category: Python

Aayush Srivastava takes us through one of the classics of classification:

In the realm of machine learning classification, model evaluation is an essential step to assess the performance and effectiveness of various algorithms. One widely-used tool for this purpose is the Area Under the Receiver Operating Characteristic Curve (AUC-ROC curve). In this blog, we will delve into the significance of the AUC-ROC curve, how it is calculated, and why it is an invaluable metric for evaluating classification models.

In this article, we will discuss the performance metrics used in the classification and also explore the implications of using two, namely AUC and ROC. Here is an overview of the important points that we will discuss in the article. 

The fun anecdote around ROC curves is that their name actually makes sense if you know the origin: it came out of the British army in World War II, where they tracked how their radar operators classified blips as German aircraft or noise (e.g., flocks of birds). The radar receiver operators had certain characteristics, where some were more effective at separating actual threats from noise, hence the Receiver Operating Characteristic curve.

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John Mount has some thoughts for us:

I want to collect some “great things to know about linear Markov chains.”

For this note we are working with a Markov chain on states that are the integers 0 through k (k > 0). A Markov chain is an iterative random process with time tracked as an increasing integer t, and the next state of the chain depending only on the current (soon to be previous) state. For our linear Markov chain the only possible next states from state i are: i (called a “self loop” when present), i+1 (called up or right), and i-1 (called down or left). In no case does the chain progress below 0 or above k.

Click through for notes on two variants of this sort of linear Markov chain, as well as a pair of appendices containing derivation notes and Python code.

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