January 2019
Intermediate to advanced
390 pages
9h 16m
English
Content preview from Hands-On Artificial Intelligence for IoT
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Feature scaling to resolve uneven data scale
The data that's collected normally doesn't have the same scale; for example, one feature may be varying in the range 10–100 and another one may be only distributed in range 2–5. This uneven data scale can have an adverse effect on learning. To resolve this, we use the method of feature scaling (normalization). The choice of normalization has been found to drastically affect the performance of certain algorithms. Two common normalization methods (also called standardization in some books) are as follows:
- Z-score normalization: In z-score normalization, each individual feature is scaled so that it has the properties of a standard normal distribution, that is, a mean of 0 and variance of 1. If
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