- Supervised learning refers to the process of using a set of samples of known classes to adjust the parameters of a classifier to achieve the required performance.
- Unsupervised learning refers to solving various problems in pattern recognition based on training samples whose classes are unknown .
- Common supervised learning algorithms: LR, NB, KNN, SVM, RF, GBDT, XgBoost, AdaBoost, etc.
- Common unsupervised learning algorithms: K-means, Apriori, FP-Growth, DBSCAN, etc.
Supervised and Unsupervised Learning
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