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Feature Selection

Feature Selection

What is Feature Selection

Characterize problem with smallest set of features,

Feature Selection Methods

Adding Features

New Features derived from existing features

Removing Features

  • Features that are very correlated
  • Features with a lot of missing values
  • Irrelevant features : ID, row number, etc.

Combining Features

Recoding Features

Examples

  • Discretization: re-format continuous feature as discrete
  • Customer’s age à {teenager, young, adult, senior}

Breaking Up Feature

Feature Selection Summary

  • Goal: Select Smallest set of features that best captures data for application
  • Domain Knowledge is important
  • Also known as “Feature Engineering”

Feature Transformation

Feature transformation involves mapping a set of values for the feature to a new set of values to make the representation of the data more suitable or easier to process for the downstream analysis

1) Scaling

  • Changing the range of values for a feature to another specified range
  • Done to avoid allowing features with large values to dominate the analysis results

Scaling to a range

To perform scaling is to map all values of a feature to a specific range such as between 0 an 1

Zero – Normalization / Standardization

Transform the features such that the results have zero mean and unit standard deviation

2) Filtering

A low pass filter removes components above a certain frequency allowing the rest to pass through unaltered

Remove grainy appearance in images

Filter noise from audio signal

3) Aggregation

Combines values for a feature in order to summarize the data or to reduce variability

Feature Transformation Summary

  • What: Map feature values to new set of values
  • Why: Have data in format suitable for analysis
  • Caveat: Take care not to filter out important characteristics of data

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