Abstract: Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

Abstract: A binary outcome of an activity is predicted based on samples of the activity. The activity is characterized by features that can take on any of a set of mutually exclusive levels. An initial candidate pool of terms is selected. The terms are feature levels or combinations of feature levels. The candidate terms are used to create two ordered pools of terms, one including terms that are positively predictive and another including terms that are negatively predictive. The terms in each pool are ordered by strength of predictiveness and diversity of predictiveness relative to terms higher in the order. A final set of terms is selected by combining terms from these two pools.

Abstract: Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

Abstract: A binary outcome of an activity is predicted based on samples of the activity. The activity is characterized by features that can take on any of a set of mutually exclusive levels. An initial candidate pool of terms is selected. The terms are feature levels or combinations of feature levels. The candidate terms are used to create two ordered pools of terms, one including terms that are positively predictive and another including terms that are negatively predictive. The terms in each pool are ordered by strength of predictiveness and diversity of predictiveness relative to terms higher in the order. A final set of terms is selected by combining terms from these two pools.