What are the limitations of Deep Learning as compared to other Machine Learning techniques?
- Easier interpretability and requires more data
- More interpretable and less efficient
- Requires less data and is more complex
- Requires more data and is often less interpretable
Deep Learning typically requires more data for effective training and often results in models that are less interpretable compared to traditional Machine Learning models.
Loading...
Related Quiz
- Imagine you need to classify documents but have only a few labeled examples. How would you leverage semi-supervised learning in this scenario?
- In a situation where the assumption of linearity in Simple Linear Regression is violated, how would you proceed?
- In what situations would RMSE be a more appropriate metric than MAE?
- You are tasked with reducing the dimensionality of a dataset with multiple classes, and the within-class variance is very high. How would LDA help in this scenario?
- You are faced with a multi-class classification problem. How would the choice of K and distance metric affect the KNN algorithm's ability to differentiate between the classes?