Classifiers and Thresholds

Build a classifier to simulate a custom machine and learn the importance of thresholds through the process.

Today, we heavily depend on machines because they’re cost and time-efficient compared to humans. From farms to industries, machines have become so essential that we can’t imagine a world without them. Further, integrating artificial intelligence into modern machines has opened new horizons. In recent years, we’ve witnessed a remarkable surge in technological advancements, particularly in the field of machine learning. This progress is considered to have outpaced the innovations of previous centuries and is considered likely to remain at the forefront of human achievement for the foreseeable future.


Every machine takes an input, performs its respective function on that input, and produces an output. When this machine is configured/trained to predict a category/class label from a prespecified finite set of categories, it’s called a classifier.

Classification example

Let’s say we have a scenario where, for a given set of inputs, say xx; the machine outputs either 11 or 00 as follows.

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