Päätösrajoja

Päätösrajoja refers to decision boundaries in the context of machine learning and statistical classification. A decision boundary is a hypersurface that partitions the underlying feature space into discrete regions, with each region assigned to one of the classes. In simpler terms, it's the line or surface that separates different categories of data points. When a new data point is presented, its location relative to the decision boundary determines which class it is predicted to belong to.

The shape and position of the decision boundary are determined by the classification algorithm used and the data it was trained on. For instance, linear classifiers like logistic regression or support vector machines with a linear kernel create linear decision boundaries (lines in 2D, planes in 3D, or hyperplanes in higher dimensions). More complex algorithms, such as decision trees or neural networks with non-linear activation functions, can create highly non-linear and intricate decision boundaries, allowing them to model more complex relationships in the data.

The goal in training a classification model is often to find an optimal decision boundary that best separates the classes, minimizing misclassification errors. The effectiveness of a classifier is directly related to how well its decision boundary generalizes to unseen data. Overfitting can lead to a decision boundary that is too complex and fits the training data too closely, resulting in poor performance on new samples. Conversely, an overly simple boundary might underfit the data, failing to capture the underlying structure.