👉 Selection computing is an algorithm used primarily in machine learning, particularly for handling high-dimensional data where the number of features far exceeds the number of samples. It's an extension of decision tree algorithms, focusing on constructing a set of optimal decision trees that collectively maximize the predictive accuracy. The process begins by selecting features (or variables) to split on at each node of the tree, aiming to reduce impurity or increase homogeneity in the subsets of data. This is typically done using criteria like Gini impurity or information gain, which measure how well a split separates the data into distinct classes. Once the initial splits are made, the algorithm recursively applies the same process to each subset until a stopping criterion is met, such as reaching a maximum tree depth or having all instances in a node belong to the same class. The final model is a collection of decision trees, often combined using techniques like bagging or boosting, to improve robustness and generalization on unseen data. Selection computing is especially useful in scenarios where traditional methods struggle due to the curse of dimensionality or when dealing with noisy data.
