14. Feature Selection.pptx

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Feature selection is one of data science concept of practice.

Data & Analytics

12. Feature Selection

FEATURE SELECTION
Select the input attributes that matter
1. When the number of features or attributes is really large -> computational
expense is a criterion
2. Attributes are confounded

Methods

PRINCIPAL COMPONENT ANALYSIS

PCA

Sample dataset

RM Process

Eigenvectors

INFORMATION THEORY–
BASED FILTERING

CHI-SQUARE-BASED
FILTERING

Chi-Square
fo is the observed frequency and fe is the expected frequency

WRAPPER-TYPE
FEATURE SELECTION

Concept
Iteratively chooses features to add or to remove from the current attribute pool based on whether the
newly added or removed attribute improves the accuracy.

Backward elimination

RM Process

RM Process

Feature selection

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14. Feature Selection.pptx

1. 12. Feature Selection

2. FEATURE SELECTION Select the input attributes that matter 1. When the number of features or attributes is really large -> computational expense is a criterion 2. Attributes are confounded

4. PRINCIPAL COMPONENT ANALYSIS

6. Sample dataset

8. Eigenvectors

9. INFORMATION THEORY– BASED FILTERING

10.

11.

12. CHI-SQUARE-BASED FILTERING

13. Chi-Square fo is the observed frequency and fe is the expected frequency

14.

15.

16.

17. WRAPPER-TYPE FEATURE SELECTION

18. Concept Iteratively chooses features to add or to remove from the current attribute pool based on whether the newly added or removed attribute improves the accuracy.

19. Backward elimination

22. Feature selection