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What are basic difference between algorithms and models Is regression SVM random forest decision trees algorithms or model

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    – noe
    Nov 25, 2023 at 10:03

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A machine learning algorithm is a more or less an abstract set of rules, mathematical functions, or steps that are required to formally state how the computer can learn to make, e.g., predictions. "Model" typically implies that a training process for determining weights (unknown model parameter, e.g., the coefficients of a polynomial that is fitted to some training data) already was performed.

Note, however, that in the literature or in the web notions are not always well defined or used consistently: there is probably consensus about "algorithm" but sometimes "model" is also used instead of "architecture" (in the deep learning community). Unfortunately, there are a few community-dependent differences (e.g., sometimes some notions have a slightly different connotation or meaning in the deep learning community as compared to the statistical learning community; the notions of loss vs cost is another such example).

So, in your case, SVM or random forest decision trees are algorithms. Once you implemented them in a computer program (or picked an implementation) and trained them you have a ML model with which you can perform predictions or make inference (inference is yet another word that is used differently in the deep learning community, by the way: there, it is used equivalently to "predictions"). Calling it a "trained model" has some redundancy but sometimes makes communication easier.

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