Basic Machine Learning Alogrithms

Here is a table of the machine learning algorithms, along with whether they are supervised or unsupervised learning algorithms:

AlgorithmSupervisedUnsupervisedLinear regressionSupervisedNoDecision treesSupervisedNoRandom forestSupervisedNoAda boostSupervisedNoGradient boostSupervisedNoLogistic regressionSupervisedNoK-nearest neighbors (KNN)SupervisedNoSupport vector machines (SVM)SupervisedNoK-meansUnsupervisedYesCollaborative filteringUnsupervisedYesPrincipal component analysis (PCA)UnsupervisedYes

In supervised learning, the algorithm is given labeled data, which means that the data is paired with the correct output. The algorithm then learns to map the input data to the output data. In unsupervised learning, the algorithm is not given labeled data. The algorithm must learn to find patterns in the data without any guidance.

Here is a table of the above machine learning algorithms whether they can be used for regression or classification:

AlgorithmRegressionClassificationLinear regressionYesNoDecision treesYesYesRandom forestYesYesAda boostYesYesGradient boostYesYesLogistic regressionYesYesK-nearest neighbors (KNN)YesYesSupport vector machines (SVM)YesYesK-meansNoNoCollaborative filteringNoNoPrincipal component analysis (PCA)NoNo

As you can see, all of the algorithms except for K-means, collaborative filtering, and PCA can be used for both regression and classification. However, some algorithms are better suited for one task than the other. For…