When comparing logistic regression and k-nearest neighbor (k-NN), which of the following is true?

Logistic regression and k-nearest neighbor (k-NN) have fundamentally different approaches to classification, particularly in how they make predictions. The correct statement that logistic regression typically takes longer to make predictions than k-NN can be attributed to the mechanics of both algorithms.

Logistic regression is a parametric model that requires the computation of coefficients based on the training data. When making predictions, it applies a mathematical function (the logistic function) to these coefficients along with the input features, which can involve some computations but is relatively quick once trained. However, it does require the initial phase of fitting the model to the training data, which can add to its overall time spent in more complex datasets.

In contrast, k-NN is a non-parametric algorithm that does not involve a training phase in the traditional sense. It stores the training data and makes predictions based on the proximity of new data points to the existing data points. When making predictions, k-NN calculates distances to all training instances and identifies the closest neighbors, which can be computationally intensive depending on the size of the dataset. However, because of its nature, k-NN can be quicker when predicting single instances if the training data is not too large, as it doesn’t necessitate prior