The K-Nearest Neighbors (KNN) model is crucial for classifying latitude and longitude data based on proximity to other data points. However, determining the optimal number of neighbors (K) is challenging. To address this, we adapt the elbow method used for clustering algorithms.
First, we ensure the dataset includes latitude and longitude variables and then create a new feature combining these values for more spatial relationships.
Next, we select a range of potential K values and train the KNN model for each value. Evaluate performance using metrics like accuracy, precision, recall, and F1 score.
Then, Plot the performance metrics against the range of K values. Initially, performance improves with more neighbors, but at a certain point, adding more does not significantly improve it. This point is the elbow.
Identify the K value at the elbow point to determine the optimal number of neighbors. This fine-tunes accuracy and achieves precise classification outcomes for latitude and longitude data.
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