What is linear discriminant analysis?
Logistic regression is a classification algorithm traditionally limited to two types of classification problems. If you have more than two classes, the linear discriminant analysis algorithm is the preferred linear classification technique.
LDAThe representative is very simple. It contains statistical properties of the data, calculated for each class. For a single input variable, this includes:
- The average of each category.
- The variance calculated in all categories.
The prediction is performed by calculating the discriminant value of each class and predicting the class having the largest value. This technique assumes that the data has a Gaussian distribution (bell curve), so it is best to remove outliers from the data in advance. It is a simple and powerful method for classifying predictive modeling problems.
Baidu Encyclopedia version
Linear discriminant analysis (LDA) is a generalization of Fisher's linear identification method, which uses statistics, pattern recognition and machine learning methods to try to find a linear combination of the characteristics of two types of objects or events. To be able to characterize or distinguish them. The resulting combination can be used as a linear classifier or, more commonly, for dimensional reduction processing for subsequent classifications.
Linear discriminant analysis (LDA), normal discriminant analysis (NDA) or discriminant function analysis is a generalization of Fisher linear discriminant, which is a method for statistics, pattern recognition and machine learning, used to find characterization or separation. A combination of linear features of one or two features. More classes of objects or events. The resulting combination can be used as a linear classifier or, more commonly, to reduce the dimension before later classification.