Neural Networks

This chapter explores neural networks, focusing on their applications and underlying principles. Neural networks have gained immense popularity due to their flexibility and accuracy in supervised data mining tasks. They can effectively handle problems with categorical and continuous target variables, making them a versatile tool in predictive modeling. The chapter introduces the concept of artificial neural networks, which mimic the structure and function of human brain neurons. The mathematical model of a neuron, first proposed by McCulloch and Pitts, serves as the foundation for neural networks. However, early attempts to implement neural networks faced challenges, leading to a period of reduced interest. The breakthrough came in the 1980s with the development of algorithms like backpropagation, which enabled the estimation of weights in multilayer networks. The chapter discusses the learning process for neural networks, which involves adjusting the model weights iteratively to minimize an error function. Different activation functions are explored, each influencing the output of the neurons. Notably, the ReLU activation function enabled the development of deep learning models with three or more hidden layers. An example of a single-layer artificial neuron demonstrates the calculations with various activation functions. This is followed by an example of a multilayer perceptron, showcasing the real power of neural networks with multiple layers and nodes. Neural network applications using KNIME are illustrated in the context of credit screening and predicting used car prices. The chapter also emphasizes the importance of proper data preparation, including normalization and dealing with oversampling in the context of classification problems. Overfitting, a common challenge in neural networks, is discussed, and techniques to mitigate it are presented. The chapter provides a comprehensive overview of neural networks, highlighting their strengths and challenges. Neural networks offer great potential for complex and non-linear problems but require careful considerations in data preparation, model complexity, and validation to ensure reliable and accurate predictions.