How can we evaluate and compare multiple models, and select the best one for statistical analysis or machine learning? Simple models may fail to fit the data well, while overly complex models may suffer from "overfitting", reducing their predictive performance on future data.
To address the limitation in Dijkstra's Algorithm, which cannot handle paths with **negative** edge weights, the Bellman-Ford Algorithm was developed. The Bellman-Ford Algorithm performs $V - 1$ relaxation operations for $V$ nodes, repeatedly relaxing all edges to continuously improve and find the shortest paths.
K-means clustering, often referred to in Chinese as the "k-means algorithm", is a method used to partition $n$ data points into $k$ clusters. Each point is assigned to the cluster whose center (centroid) is closest to it.
The **Greedy Algorithm** is an approach that solves problems by **choosing the option that appears most favorable at each step**. It makes simple and intuitive decisions based on the **locally optimal choice** among all available options at the moment, with the expectation that these local choices will accumulate into a globally optimal solution, continuing this process until a termination condition is met.
