The k-nearest neighbor classifier is a simple machine learning algorithm that can be used to classify data points into one of two classes, 0 or 1. The input to the classifier is a set of data points, each with two features (x1 and x2). The output of the classifier is a set of labels, each corresponding to the class (0 or 1) that the data point should be classified as.
To design a machine learning algorithm to automatically detect plagiarism, you would need to first gather a set of training data. This data would need to include a wide variety of text samples, both original and plagiarized. Once you have this data, you would then need to design a algorithm that can learn to distinguish between the two. This algorithm would need to be able to identify common patterns in the text that are indicative of plagiarism.
This problem asks for the implementation of a k-means clustering algorithm. The input is a dataset of points in 2D space, and the output is a set of k cluster centers .
This technical problem asks for the design of a machine learning algorithm to predict the likelihood of a person having a heart attack. The input data for the algorithm would include information about the person's age , weight, gender, family history of heart disease, and other relevant health data. The output of the algorithm would be a prediction of whether or not the person is likely to have a heart attack, based on the input data.