Mindmap
Data reduction by sampling
Inhalt der Mindmap
Data reduction by sampling
REASONS TO CHOOSE SAMPLING?
- • To improve runtime performance
- HOW?
- By estimating models based on a subset of the data.
- Models estimated from a sample may be as accurate as those derived from the full dataset
- (if the sample represents the population adequately)
- Models estimated from a sample may be as accurate as those derived from the full dataset
- By estimating models based on a subset of the data.
- HOW?
- For a more focused analysis
- By selecting groups of related records
- EG
- All the properties in a specific neighbourhood
- EG
- By selecting groups of related records
- • To improve runtime performance
TYPES OF SAMPLES
- Random sampling
- Stratified sampling
- Systematic or 1-in-n sampling
- For non-random (structured) samples
In IBM SPSS Modeler
- Sample node
- Used to select a subset of records for analysis,
- OR to specify a proportion of records to discard
- Sample node
Informationen
- Beschreibung
- Data Mining Mindmap am Data reduction by sampling, erstellt von sue san am 09/02/2017.
- Erstellt von
- sue san
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