What is the primary reason for using Random Forests over a single Decision Tree in many applications?
- Faster training time
- Increased accuracy
- Lower memory usage
- Simplicity
Random Forests are preferred due to their increased accuracy over single Decision Trees. They work by aggregating the predictions of multiple trees, which reduces overfitting and results in better overall performance.
Loading...
Related Quiz
- A data scientist notices that their model performs exceptionally well on the training set but poorly on the validation set. What might be the reason, and what can be a potential solution?
- Why might a deep learning practitioner use regularization techniques on a model?
- Which type of autoencoder is designed specifically for generating data that is similar but not identical to the training data?
- In the context of the bias-variance trade-off, which one is typically associated with complex models with many parameters?
- Imagine a scenario where multiple instruments play simultaneously, and you want to isolate the sound of each instrument. Which algorithm would be most appropriate for this task?