Feature Selection. This module refers to the process of selecting the important features (dimensions) to contribute the most to output of a predictive analytics model. (Price: USD 1.5M)
Feature Selection. Feature selection was conducted using a meta-transformer built on a Random Forest (RF) algorithm4 which assigns weights to the features and ranks them according to their relative importance. The maximum number of features to be selected by the estimator was set to the default value (i.e. the square root of the total number of features) in order to identify all important variables that contribute to the risk prediction of mental health and QoL 4 Xxxx, Xxxxxx, Xxx Xxxx, and Xxx Xx. "Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features." Knowledge-based systems 95 (2016): 1-11. deterioration. The feature selection scheme was incorporated into the ML-based pipeline alongside the classification algorithm to select only the relevant features for training and testing the final model. For comparison reasons, we also set the parameter of maximum features to be selected by the estimator equal to 15 and 20, respectively, to keep the most significant and highly ranked variables for building the classification models. The obtained results were comparable with the training and testing results based on the initial default settings, suggesting a robust and generalizable ML-based prediction models for the identification of clinically important risk factors. Model training and validation Cross-validation: evaluating model performance. To address the rather common problem of model overfitting in machine learning applications in clinical research we adopted a cross- validation scheme with holdout data for the final model evaluation. Model overfitting occurs because a model that has less training error (i.e. misclassifications on training data) can have poor generalization (expected classification errors on new unseen data) than a model with higher training error. As a result, we took extra steps to avoid partially overlapping subsets of cases by splitting our dataset into training and testing subsets with a validation set. Hence, model testing was always performed on unseen cases which were not considered during the training phase and, consequently, did not influence the feature selection process. This procedure helps to minimize misclassifications on the training phase while also ensuring lessening of generalization errors. Classification with Balanced Random Forest algorithm. Univariate data imputation, feature selection and class imbalance handling were considered as a chain of transforms and estimators to build the compo...
Feature Selection. D4.1 carried out a comprehensive requirements gathering exercise, notably with deliverables D2.3 and D2.4. From these we produced a short-list of key features that members of a PICOS community value most. The features were derived from our examination of all our reference communities, but in D4.1 the analysis focused heavily of this first prototype and the pressing needs of the angling community. We began by expressing these features in terms of the benefit that they offer members. Then we described each feature in greater detail (see Appendix C), indicating the implications for an implementer of a PICOS community. D4.2 has enhanced and extended our understanding of these features, despite no significant new features being introduced.
