Think of this as the one-stop-shop/dictionary/directory for your machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms, including useful infographics to help you know when to use each one (if available). 101 Machine Learning Algorithms Each of the accordian drop downs are embeddable if you want to take them with you. All you have to do is click the little 'embed' button in the lower left hand corner and copy/paste the iframe. All we ask is you link back to this post. By the way, if you have trouble with Medium/TDS, just throw your browser into incognito mode. Classification Algorithms
Any of these classification algorithms can be used to build a model that predicts the outcome class for a given dataset . The datasets can come from a variety of domains. Depending upon the dimensionality of the dataset, the attribute types, sparsity, and missing values, etc., one algorithm might give better predictive accuracy than most others. Let's briefly discuss these algorithms. (18)
Regression Analysis
Regression .. cntd
↧