kaggle-pseudo-labeling

Pseudo-labeling improves model performance by using high-confidence predictions from unlabeled test data as soft labels to retrain models, enhancing their accuracy on unseen data. It is particularly effective when the test set is significantly larger than the training set.

To use semi-supervised learning in Kaggle competitions, run the skill to add high-confidence test predictions as soft labels to your training set. This multi-round iterative process refines pseudo-labels and retrains your model using Python, numpy, pandas, and lightgbm.

You should use pseudo-labeling when you have a well-validated ensemble prediction and a test set significantly larger than your training set. This semi-supervised approach leverages unlabeled data to overcome the challenge of limited labeled data.

Yes, pseudo-labeling works with LightGBM and sklearn models. The process applies semi-supervised learning techniques to leverage unlabeled test data, requiring these specific dependencies for data manipulation and machine learning tasks.

The main limitation of pseudo-labeling is its reliance on well-validated ensemble predictions; using low-confidence test predictions as soft labels can degrade model performance. It is only suitable for scenarios with large unlabeled test sets.