If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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