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Regression is similar to classification with one difference – the function f that we want to model maps the feature space not to a set of classes but for example to an ordinary number that is it is a function in the mathematical sense we are usd to. For example we want to estimate the amount of money that needs to be loadd into an ATM in order to have enough for a week basd on historical data for this and / or other similar ATMs. Or say predict the air temperature for tomorrow. Regression models as it has probably become clear are often usd in forecasting. From a technical point of view classification and regression are relatd tasks. And many types of models for example the same neural networkscan be usd both for classification problems and for regression problems.

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Embedding Embedding sometimes translatd as inclusionis a special class of models designd to reduce the dimension of the feature space of an object. Often embedding is placd in front of another model for example in front of a classifier and a kind of pipeline is obtained. Feature space dimensionality reduction Sounds mysterious? I’ll try to give a simple Barbados Email List example. Let’s say our model is designd for the semantic classification of natural language texts. The text is made up of words. So we ned to be able to represent words as sequences of numbers. You can solve the problem head-on and choose as a representation a vector of Unicode character codes paddd with zeros to a fixd dimension.

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This is a bad option because in this case for example the verb to forge which is not relatd in any wayand the name of the river Lovat will have very close representations and denoting the same words airplane BTC Email List and airplane- very distant. You can do it differently. Let the number of dimensions of our feature space correspond to the number of words in the Russian language. Then we can single out a separate dimension for each specific word. And represent the word as a vector which has the value in its its dimension and all other components are zero.

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