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Ch#4

1. What is data reduction?

Answer:
Data reduction is the process of reducing the volume of data while producing the same or similar analytical results.

2. Why do we need data reduction?

Answer:

  • Improves model performance (speed & accuracy)

  • Helps in data visualization

  • Reduces dimensionality

  • Removes noise

  • Leads to simpler, faster, and more accurate models

3. What is feature selection (aka attribute/variable selection)?

Answer:
It’s the process of selecting an optimal subset of features from the data that contribute most to the model, based on a specific evaluation criterion.

5. What are the main techniques for feature selection (data reduction)?

Method Description Tools/Details
Wrapper Method Uses a classifier to evaluate feature subsets based on their performance - Generates all possible subsets- Uses a search technique to find best one
Filter Method Ranks features using an attribute evaluator and selects top-ranked ones - Doesn’t rely on classifier- Example in WEKA: InfoGainAttributeEval + Ranker

6. Difference between Wrapper and Filter methods?

Feature Wrapper Method Filter Method
Based on Classifier performance Statistical evaluation
Speed Slower (computationally expensive) Faster
Accuracy Generally more accurate May not consider interaction between features
Tool Example Classifier + subset evaluator InfoGain + Ranker in WEKA

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