Compensation techniques
Dealing with noisy data are crucial in machine learning to improve model robustness and generalization performance. Two common approaches for compensating for noisy data are cross-validation and ensemble models.
- Cross-validation: Cross-validation is a resampling technique used to assess how well a predictive model generalizes to an independent dataset. It involves partitioning the dataset into complementary subsets, performing training on one subset (training set) and validation on the other (validation set). This process is repeated multiple times with different partitions of the data. Common cross-validation methods include k-fold cross-validation and leave-one-out cross-validation. By training on different subsets of data, cross-validation helps in reducing the impact of noise in the data. It also aids in avoiding overfitting by providing a more accurate estimate of the model’s performance.
- Ensemble Models: Ensemble learning involves combining multiple individual models to improve predictive performance compared to any single model alone. Ensemble models work by aggregating the predictions of multiple base models, such as decision trees, neural networks, or other machine learning algorithms. Popular ensemble techniques include bagging (Bootstrap Aggregating), boosting, and stacking. By combining models trained on different subsets of the data or using different algorithms, ensemble models can mitigate the impact of noise in the data. Ensemble methods are particularly effective when individual models may be sensitive to noise or may overfit the data. They help in improving robustness and generalization performance by reducing the variance of the predictions.
How to handle Noise in Machine learning?
Random or irrelevant data that intervene in learning’s is termed as noise.