Which technique is often used to handle scalability in machine learning models?
- Dimensionality Reduction
- Ensemble Learning
- Feature Engineering
- Reinforcement Learning
Dimensionality Reduction techniques, like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), are often used to handle scalability. They reduce the number of features while retaining essential information, making large datasets more manageable for machine learning models.
Loading...
Related Quiz
- How does deep learning contribute to high-frequency trading strategies?
- What is the fundamental difference between symbolic AI and connectionist AI regarding knowledge representation?
- Which philosophical concept questions the feasibility of creating a superintelligent AI that has values aligned with human values?
- An AI model developed for facial recognition is found to have significantly lower accuracy for certain ethnic groups. How would you approach correcting this bias without compromising the model’s overall accuracy?
- What role does the concept of "justice" play in developing ethical AI models?