mitteühikujoonelisi

mitteühikujoonelisi is an Estonian term that translates to "non-linearly separable" in English. This concept is fundamental in the field of machine learning, particularly in classification tasks. A dataset is considered non-linearly separable if there is no single straight line (or hyperplane in higher dimensions) that can perfectly divide the data points belonging to different classes. Imagine plotting data points on a graph where some are red and some are blue. If you can draw a straight line to put all the red points on one side and all the blue points on the other, the data is linearly separable. If, however, the points are mixed in a way that a straight line cannot achieve this separation, the data is non-linearly separable.

This non-linear separability poses a challenge for simple linear classifiers, such as the perceptron or basic logistic regression, which rely on finding a linear decision boundary. To address this, more complex models are required. These include algorithms like Support Vector Machines (SVMs) with non-linear kernels, decision trees, random forests, and neural networks. These models are capable of learning intricate, curved decision boundaries that can effectively separate non-linearly separable data. Recognizing whether a dataset is linearly or non-linearly separable is often an initial step in choosing the appropriate machine learning algorithm for a given problem.