Decision boundaries in machine learning are lines or curves that separate different classes of data. They are used to classify data points and are a key component of many supervised learning algorithms. Decision boundaries are used to create models that can accurately predict the class of a given data point.
A decision boundary is created by a machine learning algorithm, which is trained on a set of labeled data points. The algorithm learns which features are important for distinguishing between different classes of data. It then uses these features to draw the decision boundary, which separates the data points into two or more classes.
For example, consider a dataset of two-dimensional points representing different types of flowers. The dataset contains points labeled as either “rose” or “daisy”. A machine learning algorithm can be used to draw a decision boundary that separates the points into two classes. The decision boundary is a line that best separates the points into two classes.
Decision boundaries are used in a variety of machine learning algorithms, including classification and regression. In classification, decision boundaries are used to separate different classes of data. In regression, decision boundaries are used to separate different values of a continuous variable.
Decision boundaries can also be used to identify outliers or anomalies in a dataset. For example, a decision boundary can be used to identify points that do not fit the general pattern of the data.
Decision boundaries are an important part of many machine learning algorithms, as they are used to create models that can accurately classify data points. They are also used to identify outliers and anomalies in a dataset. Understanding how decision boundaries work is essential for creating effective machine learning models.