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You're reading from Machine Learning with Swift

Product typeBook

Published inFeb 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781787121515

Edition1st Edition

Languages

Swift

Tools

iOS

Concepts

Machine Learning

Authors (3):

Jojo Moolayil

Alexander Sosnovshchenko

Oleksandr Baiev

View More author details

KNN pros

It's simple to implement if you are not going for optimized versions which use advanced data structures.
It's easy to understand and interpret. The algorithm is well studied theoretically, and much known about its mathematical properties in different settings.
You can plug in any distance metric. This allows working with complex objects, like time series, graphs, geographical coordinates, and basically anything you can define distance metric for.
Algorithms can be used for classification, ranking, regression (using neighbors average or weighted average), recommendations, and can even provide (a kind of) probabilistic output—what proportion of neighbors voted for this class.
It's easy to incorporate new data in the model or remove outdated data from it. This makes KNN a good choice for online learning (see Chapter 1, Getting Started with Machine Learning) systems.

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Machine Learning with Swift

Published in: Feb 2018Publisher: PacktISBN-13: 9781787121515

Authors (3)

Jojo Moolayil

Jojo Moolayil is a data scientist, living in Bengaluru—the silicon valley of India. With over 4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT. Jojo was born and raised in Pune, India and graduated from University of Pune with a major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT. To cement his foundations in industrial IoT and scale the impact of the problem solving experiments, he joined a fast growing IoT Analytics startup called Flutura based in Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his problem solving skills for M2M and Industrial IoT while working for the world's leading manufacturing giant and lighting solutions providers. His quest for solving problems at scale brought the 'product' dimension in him naturally and soon he also ventured into developing data science products and platforms. After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT, that is, G.E. in Bangalore, where he focused on solving decision science problems for Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data science and decision science products and platforms for Industrial IoT.
Read more about Jojo Moolayil

Alexander Sosnovshchenko

Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.
Read more about Alexander Sosnovshchenko

Oleksandr Baiev

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