Defining a task and learner to implement k-nearest neighbors (k-NNs) in mlr3

The k-nearest neighbors (k-NN) classification is a non-parametric ML algorithm used for classifying data points based on their proximity to other labeled data points. The algorithm determines the class membership of an unlabeled data point by examining the classes of its k-NNs in the feature space. The dataset consists of labeled data points, where each data point has a set of features (attributes) and belongs to a specific class or category. The value of k represents the number of nearest neighbors to consider for classification. It is typically chosen based on cross-validation or other model selection techniques. The algorithm measures the distance between the unlabeled data point and all the labeled data points in the feature space. The most commonly used distance metric is Euclidean distance. The k data points with the shortest distances to the unlabeled point are identified as its nearest neighbors. The...