Fig. 1

Common ML algorithms. a Support vector machine (SVM), a widely used supervised learning method, generates a hyperplane in higher-dimensional feature space to maximize the largest minimum distance between the separate labeled support vectors. b K-nearest neighbor classification (KNN), a instance-based learning (lazy learning), classifies objects on the based of closet training data in feature space by assigning a label based on the most dominant class of its k nearest neighbors (here, k = 3). c The random forest algorithm, an ensemble classifier, generates classifiers that are known as decision trees, which utilizes input traits as branch nodes to resemble a tree structure, to differentiate the training data into “leaves” referring to the class that terminates a series of nodes and branches. It yields reliable predictions for new input by voting from an ensemble of decision trees. d Artificial neural networks (ANN), the input (far left) is linked to “artificial neuron” by means of weighted connections, after a process of the summation, the bias, and the activation function, the input propagates to the output node (far right) for classification