Final answer:
In KNN for a continuous target variable, the predicted values are computed by averaging the target values of the k-nearest neighbors. The selection of k and the distance metric are crucial for accurate predictions.
Step-by-step explanation:
How KNN Predicts Continuous Target Variables
When a continuous target variable is given, the predicted values in the k-nearest neighbors (KNN) algorithm are computed by averaging the values of the k-nearest neighbors.
KNN is a type of instance-based learning where predictions are made for a new data point by looking at the most similar historical data points. In the case of a continuous target variable, this typically means finding the k closest data points in the feature space, and computing the average of these data points’ target values. The average is then used as the prediction for the continuous target variable. It's important to select the appropriate number of neighbors (k) to balance the tradeoff between overfitting and underfitting.
Selecting the right distance metric and ensuring proper scaling of the feature data is also critical for the accurate performance of KNN.