June 2020
Intermediate to advanced
382 pages
11h 39m
English
Content preview from 40 Algorithms Every Programmer Should Know
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Understanding enabling conditions
Supervised machine learning is based on the ability of an algorithm to train a model using examples. A supervised machine learning algorithm needs certain enabling conditions to be met in order to perform. These enabling conditions are as follows:
Enough examples: Supervised machine learning algorithms need enough examples to train a model.
Patterns in historical data: The examples used to train a model need to have patterns in it. The likelihood of the occurrence of our event of interest should be dependent on a combination of patterns, trends, and events. Without these, we are dealing with random data that cannot be used to train a model.
Valid assumptions: When we train a supervised machine learning model ...
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