Abstract: A specification of a desired target field for machine learning prediction and one or more tables storing machine learning training data are received. Within the one or more tables, eligible machine learning features for building a machine learning model to perform a prediction for the target field are identified. The eligible machine learning features are evaluated using a pipeline of different evaluations to successively filter out one or more of the eligible machine learning features to identify a set of recommended machine learning features among the eligible machine learning features. The set of recommended machine learning features is provided for use in building the machine learning model.

Abstract: A pre-trained model trained to predict a measure of expected model performance based at least in part on a feature relevance score associated with a text field data type is generated. A specification of a desired target field for machine learning prediction and one or more text fields storing input content is received. A corresponding feature relevance score for each of the one or more text fields storing the input content is calculated. Based on the corresponding calculated feature relevance scores, a corresponding measure of expected model performance for each of the one or more text fields storing the input content is predicted using the pre-trained model. The predicted measures of expected model performance are provided for use in feature selection among the one or more text fields storing the input content for generating a machine learning model to predict the desired target field.