- What is an ensemble method? What are some popular ensemble methods?
Ensemble methods are used for increasing classification accuracy. An ensemble combines a series of k learned models, with the goal of creating an improved classification model. The individual classifiers vote and the ensemble returns a class label prediction based on the votes. Boosting, bagging, and random forests are popular ensemble methods.
- What is a neural network?
A neural network is a set of connected input/output units where each connection has a weight associated with it. During the learning phase, the network learns by adjusting the weights in order to be able to predict the correct class label for the input tuples. Neural networks can take a long time to train and therefore should be left for applications where this isn’t a concern.
- What are the advantages and disadvantages of neural networks?
Advantages: High tolerance to noisy data, ability to classify patterns that were not trained, well suited for continuous-valued inputs and outputs, and have been successful on real-world data such as pathology and handwritten character recognition.
Disadvantages: Long training times, poor interpretability by humans, require a number of parameters that are typically best determined empirically such as the network structure.