Results obtained from the Heart Disease dataset (Table 2) highlight that the Expectation-Maximization (EM) approach consistently outperformed alternative imputation methods-including mean, KNN, MICE, ...

A version of this article appears in print on June 9, 2025, Section A, Page 21 of the New York edition with the headline: Our Regression on Gender Is a Tragedy.

We compared 4 single-output models (kNN, regression tree, random forest and (NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance ...

KNN is also one of the most conventional nonparametric regression techniques because of its ease of use and high accuracy 14. The KNN algorithm is a simple technique that depends on k nearest data ...

KNN regression was used to extract reference values for spirometric parameters FEV 1, FVC, FEV 1 /FVC, and FEF 25–75%, considering height and age as features. The performance of KNN regression was ...

Figure 8. Experimental vs prediction for k -nearest neighbors (KNN) regression model. Predictions are good though noisy overall.

The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression ...

In this research study, the K-nearest neighbors regression (KNN regression) was used to estimate temperature, based on certain features of an interferometric sensor's spectrum. Typically, these ...