Answers.Body: <p>The problem you are examining is called <a href="https://en.wikipedia.org/wiki/Feature_selection" rel="nofollow noreferrer">feature selection</a>. There are many different techniques, but they fall broadly into three categories:</p>
  
  <ol>
  <li>Filter approaches determine which features have high information content. A common approach is to score them based on their <a href="https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow noreferrer">information gain</a>. </li>
  <li>Wrapper approaches score subsets of the features, using similar measurements. This is slower (since there are many subsets), but may yield better performance.</li>
  <li>Embedded approaches use machine learning algorithms that can dynamically select features. For example, building a <a href="https://en.wikipedia.org/wiki/C4.5_algorithm" rel="nofollow noreferrer">C4.5 decision tree</a> learner on a dataset, and then selecting just the features that actually appear in the tree, yields a set of features similar to those found by information gain.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-21T12:30:10.097Z
Answers.LastActivityDate: 2018-08-21T12:30:10.097Z
Answers.UserDisplayName: John Doucette
Body: <p>A dataset contains so many fields in which there is both relevant and irrelevant field. If we want to do a market campaigning using propensity scoring, which fields of the data set are relevant?
  How can we find which data field should be selected and can drive to the desired propensity score?</p>
  
Tags: <machine-learning><datasets>
Title: Which features of a data set can be used for market campaigning using propensity scores?
UserDisplayName: SRJ577
ThreadText: # Which features of a data set can be used for market campaigning using propensity scores?
  
  (Asked by: username_0 on 2018-08-21)
  
  A dataset contains so many fields in which there is both relevant and irrelevant field. If we want to do a market campaigning using propensity scoring, which fields of the data set are relevant?
  How can we find which data field should be selected and can drive to the desired propensity score?
  
  **Tags:** machine-learning, datasets
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-08-21. Score: 1
  
  The problem you are examining is called [feature selection](https://en.wikipedia.org/wiki/Feature_selection). There are many different techniques, but they fall broadly into three categories:
  
  1. Filter approaches determine which features have high information content. A common approach is to score them based on their [information gain](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence).
  2. Wrapper approaches score subsets of the features, using similar measurements. This is slower (since there are many subsets), but may yield better performance.
  3. Embedded approaches use machine learning algorithms that can dynamically select features. For example, building a [C4.5 decision tree](https://en.wikipedia.org/wiki/C4.5_algorithm) learner on a dataset, and then selecting just the features that actually appear in the tree, yields a set of features similar to those found by information gain.
  
  
  ---
  
  