So far, we have mostly covered classic and tree-based algorithms for both regression and classification. We saw that the ensemble technique showed the best performance compared to classic algorithms. However, there are other algorithms, such as one-vs-rest algorithm, which work for solving classification problems using other classifiers, such as logistic regression.
Apart from this, neural-network-based approaches, such as multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN), can also be used to solve supervised learning problems. However, as expected, these algorithms require a large number of training samples and a large computing infrastructure. The datasets we used so far throughout the examples had a few samples. Moreover, those were not so high dimensional. This doesn't mean that we cannot use them to...