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A better alternative may be to learn a simpler predictor such as () = +: experiments show that this simpler predictor (called Slope One) sometimes outperforms [1] linear regression while having half the number of regressors. This simplified approach also reduces storage requirements and latency.
The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.
The capital asset pricing model uses linear regression as well as the concept of beta for analyzing and quantifying the systematic risk of an investment. This comes directly from the beta coefficient of the linear regression model that relates the return on the investment to the return on all risky assets.
The following outline is provided as an overview of, and topical guide to, machine learning: . Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1]
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These are called margin-based loss functions. Choosing a margin-based loss function amounts to choosing ϕ {\displaystyle \phi } . Selection of a loss function within this framework impacts the optimal f ϕ ∗ {\displaystyle f_{\phi }^{*}} which minimizes the expected risk, see empirical risk minimization .