What are the most common techniques for handling missing labels in your dataset?

Label imputation is a procedure where missing labels are replaced with estimated values, typically derived from existing data or assumptions. Various methods like mean, median, mode, constant, or regression can be employed for label imputation. It can be beneficial when missing labels are random and unrelated to the features or outcome. However, if missingness isn't random or the imputation method isn't suitable, label imputation can introduce noise or alter the label distribution.

To handle missing labels in datasets: 1. Deletion: Remove instances with missing labels, effective if data loss is minimal. 2. Imputation: Replace missing labels with estimated values based on other data points. 3. Data Augmentation: Generate synthetic data to fill gaps. 4. Re-weighting: Adjust the model to account for data imbalance caused by missing labels. 5. Semi-supervised Learning: Use both labeled and unlabeled data for training. 6. Multiple Imputation: Create multiple complete datasets, each with different imputations, and combine results. Select a technique based on the extent and pattern of missing data for optimal results.

Label inference involves predicting missing labels from available features using supervised or semi-supervised learning models like logistic regression, decision trees, k-nearest neighbors, or neural networks. This approach is valuable when missing labels are linked to the features and there's sufficient labeled data to train a dependable model. However, label inference can be computationally intensive and susceptible to overfitting, particularly in high-dimensional feature spaces or when labeled data is limited or noisy.

Label propagation is highly useful to enrich data and convert unlabelled to labelled data which can be further used for supervised learning. Some of the ways are listed are as follows: - Few shot learning: Leveraging a few samples of labeled data to label the unlabeled data - Similarity metrics: Using Jaccard distance, Cosine similarity, and KNN classifier to propagate labeling. - Zero-shot learning: This can be done in two ways: By using contradiction and entailment to classify unlabeled data to relevant labels or we can label the newly seen data into labeled data by the similarity of labels.

Label ignoring entails disregarding missing labels and handling the data as unlabeled while employing unsupervised learning models like clustering, dimensionality reduction, or anomaly detection. This approach is practical when the missing labels have no connection to the features or the outcome, and there's no labeled data available for imputation, inference, or propagation. Nonetheless, label ignoring can be constraining or misleading as it doesn't leverage available label information or address the primary learning objective.

For computer vision tasks, employ semi-supervised learning by leveraging the available labeled data along with unlabeled instances. Utilize self-training, where a model predicts labels for unlabeled data and iteratively refines its predictions. For tables/text techniques like label propagation or imputation to estimate missing labels based on data relationships. Carefully choose and evaluate these methods based on the specific characteristics of your dataset