Which learning tool is a parametric statistical learning method among the options?

Logistic regression is indeed a parametric statistical learning method because it assumes a specific form for the relationship between the independent variables and the dependent variable, typically modeled using the logistic function. In logistic regression, the parameters (coefficients) are estimated from the data, which allows for making inferences about the data and predictions for new observations. Since it relies on a defined mathematical formulation, it can be considered "parametric."

In contrast, the other learning tools mentioned are non-parametric methods. K-nearest neighbors relies on distance calculations among data points without assuming an underlying distribution for the data. Regression trees, which segment the predictor space into distinct regions, do not rely on parameters in the same way but instead create a model based on the data's structure. Boosting is an ensemble technique that combines multiple weak learners to improve prediction accuracy but does not fit into the parametric mold due to its focus on combining the outputs of various models rather than estimating a specific functional form.

Therefore, logistic regression stands out among the provided options as the only method that is parametric, allowing for assumptions about the distribution of data and leading to simpler interpretations of model coefficients.