What are the main challenges in unsupervised learning?

What are the main challenges in unsupervised learning?

Unsupervised learning faces several challenges, including the difficulty of evaluating model performance, the need for large amounts of data, and the potential for overfitting. Additionally, interpreting the results can be complex since there are no labeled outputs to guide the learning process.

Can you explain why evaluating model performance is difficult?

Evaluating model performance in unsupervised learning is challenging because there are no ground truth labels to compare against. Metrics like silhouette score or Davies-Bouldin index can be used, but they may not always reflect the true quality of the clustering or representation.

What are some common techniques used in unsupervised learning?

Common techniques include clustering algorithms like K-means and hierarchical clustering, dimensionality reduction methods like PCA and t-SNE, and association rule learning.

How does overfitting occur in unsupervised learning?

Overfitting in unsupervised learning can occur when a model captures noise in the data rather than the underlying structure, leading to poor generalization on new data.

What strategies can be used to mitigate these challenges?

Strategies include using cross-validation techniques, employing regularization methods, and leveraging domain knowledge to guide the learning process.