A 線形分類器 is a type of 機械学習 model used for classification tasks. It works by finding a 線形結合 of features that can separate different classes in a dataset. The core idea is to create a decision boundary—a straight line in two-dimensional space (or a hyperplane in higher dimensions)—that best separates the data points of different classes.
Linear classifiers operate under the assumption that the classes can be separated by a linear function. For example, in a 二値分類 problem, if we have two classes, the goal is to determine the weights that define a line (in 2D) or a hyperplane (in higher dimensions) such that data points from one class are on one side, and data points from the other class are on the opposite side.
一般的な線形分類器の例は次のとおりです：
- ロジスティック回帰: Although it is used for binary classification, it predicts probabilities using a logistic function.
- サポートベクターマシン （SVM）： A linear SVM finds the hyperplane that maximizes the margin between the closest data points of each class.
- パーセプトロン： An early type of ニューラルネットワーク 二値分類に使用できる。
線形分類器は、データが 線形に分離可能な場合に, meaning that it can be perfectly divided by a straight line or a hyperplane. However, they may struggle with complex datasets where the relationship between features and classes is non-linear. In such cases, techniques like kernel methods or using more complex models (like neural networks) may be preferred.