Data Sampling
Data sampling is the process of selecting a smaller part of a dataset. The goal is to analyze or train models without using the full data. The sample must represent the main dataset.
Why Data Sampling Is Important
- Reduces compute time
- Speeds up testing and experiments
- Handles large datasets
- Improves workflow when data is hard to process
Types of Data Sampling
1. Random Sampling
Select items at random. Each item has an equal chance of being chosen.
2. Stratified Sampling
Split data into groups called strata. Take samples from each group. This keeps proportions stable.
3. Systematic Sampling
Select every k th item from a list.
4. Cluster Sampling
Split data into clusters. Pick some clusters and analyze all items in them.
Sampling in Machine Learning
- Used to balance datasets
- Used to handle imbalanced classes
- Used to reduce dataset size
- Used to speed training
Balancing Methods
Undersampling
Remove samples from the majority class.
Oversampling
Add or duplicate samples from the minority class.
SMOTE
Create synthetic samples for the minority class.
Challenges
- Bad samples cause bias
- Small samples reduce accuracy
- Stratification may be required for fairness
Data Sampling in Moroccan Darija
Data sampling howa ikhraj chi parte sghira men dataset kbir. Kankhdmo biha bach ntestiw models w nser3o l process.
Types
- Random. Ikhtiyar random.
- Stratified. Kankhsmo data l groups w kandiro sample men kol group.
- Systematic. Kandiro selection kola k step.
- Cluster. Kandiro clusters w kankhtaro chi clusters kamlin.
F ML
- Balancing.
- Reduction.
- Speed training.
Conclusion
Data sampling helps you work with large datasets. It reduces cost, speeds testing, and supports balanced machine learning tasks.