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[MINI] The Elbow Method
FromData Skeptic
ratings:
Length:
15 minutes
Released:
Mar 18, 2016
Format:
Podcast episode
Description
Certain data mining algorithms (including k-means clustering and k-nearest neighbors) require a user defined parameter k. A user of these algorithms is required to select this value, which raises the questions: what is the "best" value of k that one should select to solve their problem?
This mini-episode explores the appropriate value of k to use when trying to estimate the cost of a house in Los Angeles based on the closests sales in it's area.
This mini-episode explores the appropriate value of k to use when trying to estimate the cost of a house in Los Angeles based on the closests sales in it's area.
Released:
Mar 18, 2016
Format:
Podcast episode
Titles in the series (100)
[MINI] Cross Validation: This miniepisode discusses the technique called Cross Validation - a process by which one randomly divides up a dataset into numerous small partitions. Next, (typically) one is held out, and the rest are used to train some model. The hold out set can... by Data Skeptic