Abstract: Provided are computing systems, methods, and platforms for generating training and testing data for machine-learning models. The operations can include receiving signal extraction information that has instructions to query a data store. Additionally, the operations can include accessing, using Structured Query Language (SQL) code generated based on the signal extraction information, raw data from the data store. Moreover, the operations can include processing the raw data using signal configuration information to generate a plurality of signals. The signal configuration information can have instructions on how to generate the plurality of signals from the raw data. Furthermore, the operations can include joining, using SQL code, the plurality of signals with a first label source to generate training data and testing data. Subsequently, the operations can include processing the training data and the testing data to generate the input data. The input data being an ingestible for a machine-learning pipeline.

Abstract: Provided are computing systems, methods, and platforms for a discrete-valued output classification. The operations can include obtaining a candidate threshold value for a first slice in a plurality of data slices. Additionally, the operations can include calculating, using a candidate machine-learned model and the candidate threshold value, a first performance value associated with a first risk tolerance value. Moreover, the operations can include determining, based on the first performance value, that a safeguard criterion for the first slice has not been satisfied. In response to the determination that the safeguard criterion for the first slice has not been satisfied, the operations can include performing a tradeoff logic operation to determine the final threshold value. Subsequently, the operations can include determining, using the candidate machine-learned model, whether input data is authentic based on the final threshold value.

Abstract: Provided are computing systems, methods, and platforms for generating training and testing data for machine-learning models. The operations can include receiving signal extraction information that has instructions to query a data store. Additionally, the operations can include accessing, using Structured Query Language (SQL) code generated based on the signal extraction information, raw data from the data store. Moreover, the operations can include processing the raw data using signal configuration information to generate a plurality of signals. The signal configuration information can have instructions on how to generate the plurality of signals from the raw data. Furthermore, the operations can include joining, using SQL code, the plurality of signals with a first label source to generate training data and testing data. Subsequently, the operations can include processing the training data and the testing data to generate the input data. The input data being an ingestible for a machine-learning pipeline.

Abstract: Provided are computing systems, methods, and platforms for a discrete-valued output classification. The operations can include obtaining a candidate threshold value for a first slice in a plurality of data slices. Additionally, the operations can include calculating, using a candidate machine-learned model and the candidate threshold value, a first performance value associated with a first risk tolerance value. Moreover, the operations can include determining, based on the first performance value, that a safeguard criterion for the first slice has not been satisfied. In response to the determination that the safeguard criterion for the first slice has not been satisfied, the operations can include performing a tradeoff logic operation to determine the final threshold value. Subsequently, the operations can include determining, using the candidate machine-learned model, whether input data is authentic based on the final threshold value.

Abstract: Provided are computing systems, methods, and platforms for generating training and testing data for machine-learning models. The operations can include receiving signal extraction information that has instructions to query a data store. Additionally, the operations can include accessing, using Structured Query Language (SQL) code generated based on the signal extraction information, raw data from the data store. Moreover, the operations can include processing the raw data using signal configuration information to generate a plurality of signals. The signal configuration information can have instructions on how to generate the plurality of signals from the raw data. Furthermore, the operations can include joining, using SQL code, the plurality of signals with a first label source to generate training data and testing data. Subsequently, the operations can include processing the training data and the testing data to generate the input data. The input data being an ingestible for a machine-learning pipeline.

Abstract: Provided are computing systems, methods, and platforms that automatically investigate and analyze the impact of new features or signals on the performance of a machine learning model by producing a ranked list of the most impactful features from input of a set of candidate features. In particular, one example computing system can import a training dataset associated with a user. The computing system can train a machine learning model for the training dataset and generate baseline metrics for the machine learning model. Correlations between features or signals in the training dataset can be identified and the features or signals can be grouped into clusters based on the correlations. The computing system can determine the importance of each cluster and each feature or signal. A ranked list of signals and their importances can be exported in decreasing order of machine learning model performance lift based on cluster importance and signal importance.

Abstract: Methods and systems are provided for generating, for respective mutually exclusive classes of model inputs, separate output thresholds that can be applied to the continuous-valued output of a neural network or other machine learning model in order to classify inputs in a class-sensitive manner. Such classes could be related to operational or other constraints with respect to the classifier outputs that vary across the classes of inputs. Thus, the machine learning model can be improved by using training data from all of the available classes while allowing the end performance of the model plus threshold classifier to be separately set for each input class. These automated methods for class-specific threshold setting also provide improvements with respect to accuracy, time, and cost. Also provided are methods and systems for per-class calibration of model outputs.