Different types of distance metrics used in Machine Learning. In this article, we will go through 5 of the most commonly used distance metrics.
Whether it is a supervised or unsupervised algorithm, distance metrics play an important role in Machine Learning. Different distance measures are to be chosen depending on the types of data. So it is important that we understand these metrics and how to implement/calculate them. In this article, we will go through 5 of the most commonly used distance metrics.
Euclidean distance is the straight line distance between two data points in Euclidean space. It is also called as L2 norm or L2 distance.
If p=(p1, p2) and q=(q1, q2) are two points in the Euclidean space, the Euclidean distance is given by -
If p=(p1, p2, p3), q=(q1, q2, q3) are two points in Euclidean space, the Euclidean distance is given by -
If p=(p1, p2…pn) and q=(q1, q2…qn) are two points in Euclidean space, the Euclidean distance is given by -
As you might have guessed by now, this formula looks a lot similar to the Pythagoras theorem formula. So, this is also called the Pythagoras Theorem.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
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Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also