Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated wi

Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated wi

Abstract: Systems and methods include reception of a set of data, the set of data comprising a plurality of features, building, for each of a plurality of subsets of the plurality of features, a dimension reduction model based on the subset of features and associated values of the set of data, and, for each dimension reduction model, determination of a weight associated with each of subset of features based on the dimension model, identification of a predetermined number of features associated with the highest weights, and generation, for each dimension reduction model, of a data structure comprising the predetermined number of features and the weight associated with each of the predetermined number of features. A plurality of top features are determined based on the plurality of data structures, and a supervised learning model is trained based on the plurality of top features of the set of data.