Final answer:
Adding more hidden layers and neurons to an MLP increases the time required for learning due to more weight parameters, enables more complex decision boundaries for modeling data, but also raises the risk of overfitting.
Step-by-step explanation:
Effect of Adding Hidden Layers and Neurons in MLP
When we add more hidden layers and more neurons within those layers to a Multilayer Perceptron (MLP), several things happen:
- The time required to learn the weight parameters typically increases, as there are more parameters to adjust and the complexity of the model grows. This requires more computational resources and can lead to longer training times.
- The decision boundaries that can be produced by the MLP become more complex and non-linear. This allows the network to model more complicated relationships in the data, potentially improving its ability to generalize and make accurate predictions.
- However, with an increased number of hidden layers and neurons, there is also a higher risk of overfitting. This is when a model becomes too tailored to the training data and fails to perform well on unseen data.
The use of additional layers and neurons should therefore be balanced with the need to avoid overfitting, and techniques such as regularization, dropout, or cross-validation may be employed to counteract this risk.