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In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret, [1] shorter training times, [2]
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]
High correlations between features can defeat the technique. [1] [3] ALE requires more and more uniformly distributed observations than PDP so that the conditional distribution can be reliably determined. The technique may produce inadequate results if the data is highly sparse, which is more common with high-dimensional data (curse of ...
It was proven in 2014 that the elastic net can be reduced to the linear support vector machine. [7] A similar reduction was previously proven for the LASSO in 2014. [8] The authors showed that for every instance of the elastic net, an artificial binary classification problem can be constructed such that the hyper-plane solution of a linear support vector machine (SVM) is identical to the ...
In addition to causing numerical problems, imperfect collinearity makes precise estimation of variables difficult. In other words, highly correlated variables lead to poor estimates and large standard errors. As an example, say that we notice Alice wears her boots whenever it is raining and that there are only puddles when it rains.
For highly correlated input data the one-in-10 rule (10 observations or labels needed per feature) may not be directly applicable due to the high correlation of the features: For images there is a rule of thumb that per class 1000 examples are needed. [11]
Eigengenes define robust biomarkers, [12] and can be used as features in complex machine learning models such as Bayesian networks. [13] To find modules that relate to a clinical trait of interest, module eigengenes are correlated with the clinical trait of interest, which gives rise to an eigengene significance measure.
Redundancy in the data. If the input features contain redundant information (e.g., highly correlated features), some learning algorithms (e.g., linear regression, logistic regression, and distance-based methods) will perform poorly because of numerical instabilities.