Abstract: A method includes obtaining a first dataset including a first feature set, generating a first set of feature values by providing the first dataset to a set of feature primitive stacks, and determining a reduced set of feature values based on the first set of feature values by dimensionally reducing features of the first set of feature values. The method further includes generating an intermediate set of feature values by providing a value of the first dataset and a value of the reduced set of feature values to at least one feature primitive of the set of feature primitive stacks. The method further includes updating the reduced set of feature values by dimensionally reducing features of the intermediate set of feature values and storing a second dataset including features of the intermediate set of feature values in association with the first feature set.

Abstract: A method includes obtaining a first dataset comprising a first set of features and generating a second set of features based on the first set of features set by providing the first dataset to feature primitive stacks that respectively corresponds to features of the second set of features. The method further includes determining a reduced feature set based on the second set of features and a count of correlation values between features of the second set of features, wherein the correlation values satisfy a correlation threshold. The method further includes storing the reduced feature set in a database in association with the first set of features based on a determination that a second dataset comprising the reduced feature set satisfies a set of criteria.

Abstract: A method includes receiving a plurality of original features and a ground truth, crossing over the plurality of original features to generate a plurality of current features, generating a subsequent generation of features by: calculating a current importance score of each of the plurality of current features in reference to the ground truth, calculate a parent-pool size, a population size, and a mutation rate, determining the parent-pool size number of current features having the highest current importance scores to identify a plurality of current parent features, crossing over, based on the mutation rate, the plurality of current parent features to generate the population size number of subsequent features, iterating the generating process to generate a plurality of subsequent generations of features until fitness scores of a predetermined number of consecutive generations of features stop rising and adding the parent features to the original features to form an enhanced dataset.

Abstract: Methods and systems are described herein for facilitating generation of synthetic datasets having a change point. The system may receive a command to generate a synthetic time series dataset. The system may generate data points for components of the synthetic dataset, the components including a seasonality function, a trend function, and a noise function. The system may modify the trend function to a different trend function by modifying a level or a slope of the trend function. The system may generate a change point by replacing a subset of consecutive data points generated using the trend function with consecutive data points generated using the different trend function. The system may then generate the synthetic time series dataset having a change point by combining the seasonality data points, the trend data points, and the noise data points into corresponding time slots of the synthetic time series dataset.

Abstract: Systems and methods, as described herein, relate to sequential synthesis and selection for feature engineering. A dataset may be associated with a label defining a machine-learning target attribute and a received operation that can be applied to at least one of the existing features of the dataset. One or more potential features may be generated by applying the operation to one or more existing features. For each of the one or more potential features, a feature importance algorithm may be applied to the respective feature along with the one or more existing features, generating a respective feature importance value. Respective feature importance values may be generated for each of the one or more existing features based on applying the feature importance algorithm and used to sort the potential features. A level of correlation to each of the one or more existing features may be determined to make sure it is under a threshold level to avoid new features heavily correlated to existing ones.

Abstract: Disclosed embodiments may include a system for providing a nearest neighbors classification pipeline with automated dimensionality reduction. The system may receive a dataset. The system may determine whether the dataset has a first dimensionality that exceeds a predetermined threshold. When dataset has a dimensionality that exceeds a predetermined threshold, the system may prompt a user to input an explained variance threshold ratio. The system may receive the explained variance threshold ratio. The system may iteratively perform a binary search on the dataset to determine a reduced dimensionality having a total explained variance ratio closest to but not less than the explained variance threshold ratio. The system may reduce the dataset to the reduced dimensionality to generate a reduced dataset. The system may train a machine learning model using the reduced dataset.

Abstract: In some embodiments, an exemplary method may include receiving a first dataset having a first plurality of features, performing a deep feature synthesis to synthesize a second plurality of features from the first plurality of features, separating the first plurality of features from the second plurality of features to form a third plurality of features, generating a second dataset based on the third plurality of features, running a plurality of dimensionality reductions on the second dataset to generate a plurality of reduced datasets, wherein each dimensionality reduction produces a different dimension less than a dimension of the second dataset, calculating an explained variance (EV) of each of the plurality of reduced datasets to generate a plurality of EVs, identifying a particular EV from the plurality of EVs that is a smallest EV above a threshold, and selecting a particular reduced dataset corresponding to the particular EV as a target dataset.

Abstract: Methods and systems are described herein for facilitating generation of synthetic datasets having a change point. The system may receive a command to generate a synthetic time series dataset. The system may generate data points for components of the synthetic dataset, the components including a seasonality function, a trend function, and a noise function. The system may modify the trend function to a different trend function by modifying a level or a slope of the trend function. The system may generate a change point by replacing a subset of consecutive data points generated using the trend function with consecutive data points generated using the different trend function. The system may then generate the synthetic time series dataset having a change point by combining the seasonality data points, the trend data points, and the noise data points into corresponding time slots of the synthetic time series dataset.

Abstract: Methods and systems are described herein for facilitating generation of synthetic datasets having a change point. The system may receive a command to generate a synthetic time series dataset. The system may generate data points for components of the synthetic dataset, the components including a seasonality function, a trend function, and a noise function. The system may modify the trend function to a different trend function by modifying a level or a slope of the trend function. The system may generate a change point by replacing a subset of consecutive data points generated using the trend function with consecutive data points generated using the different trend function. The system may then generate the synthetic time series dataset having a change point by combining the seasonality data points, the trend data points, and the noise data points into corresponding time slots of the synthetic time series dataset.

Abstract: Systems and methods for minimizing dimensionality of a high-dimensionality dataset during feature engineering. The system achieves this by using a tabular neural network to extract non-linear transformations of features without dramatically increasing the dimensionality of the original dataset. The system receives an original dataset for classification and a defined number of final features (e.g., dimensionality) that result from the synthetic feature creation and the neural network embedding process. Once an architecture of a model is determined, a model is fit on a synthetic feature set (e.g., a second dataset comprising synthetic features) with a given classification as a target.

Abstract: Systems and methods for novel uses and/or improvements to artificial intelligence applications, particularly in the context of practical applications featuring less complex model architectures. As one example, systems and methods described herein may achieve the technical benefits of a more complex model architecture through an ensemble of less complex models while reducing the overall training burden (e.g., in terms of computing resources, training time, and/or technical feasibility).

Abstract: Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. The system may select a statistical profile type to identify in a first dataset. The system may retrieve a statistical model corresponding to the statistical profile type. The system may select, based on a first statistical profile, a first untrained model from a first plurality of untrained models for training, wherein the first plurality of untrained models comprises respective algorithms for time-series forecasting and wherein each of the first plurality of untrained models comprises default hyperparameter tuning. The system may, based on selecting the first untrained model, tune a first hyperparameter of the first untrained model using the first dataset.

Abstract: Systems and methods for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. The systems and methods use a scoring policy based on a plurality of labeled datasets that score one or more results contained within the aggregate statistical profile. The system may dynamically identify particular criteria in statistical data that indicates an effectiveness of a given model on a given dataset. These criteria (e.g., the scoring policy) may then be updated over time as new datasets, statistical analyses, and/or aggregated statistical profiles are developed within affecting the underlying models and/or datasets.

Abstract: Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. For example, the system may apply a profiling model using a time-series embedding of the dataset combined with the aggregate statistical profile. In either case, the profiling model may be trained on the scoring policy and/or a time-series embedding of the dataset combined with the aggregate statistical profile to determine a likelihood of the effectiveness of a given model on the given dataset and/or likely hyperparameters for the given model.

Abstract: Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization.

Abstract: Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. The systems and methods described herein aim to reduce the redundancies and improve the efficiencies of model selection, model training, and/or hyperparameter selection. The systems and methods achieve this by using information about the attributes of the time-series dataset that may be used to determine a model that may be most effective at fitting a given dataset. If a model is selected prior to hyperparameter optimization, the time and resources spent training, fitting, and/or tuning models that are not selected can be avoided.

Abstract: Disclosed embodiments may include a system for providing a nearest neighbors classification pipeline with automated dimensionality reduction. The system may receive a dataset. The system may determine whether the dataset has a first dimensionality that exceeds a predetermined threshold. When dataset has a dimensionality that exceeds a predetermined threshold, the system may prompt a user to input an explained variance threshold ratio. The system may receive the explained variance threshold ratio. The system may iteratively perform a binary search on the dataset to determine a reduced dimensionality having a total explained variance ratio closest to but not less than the explained variance threshold ratio. The system may reduce the dataset to the reduced dimensionality to generate a reduced dataset. The system may train a machine learning model using the reduced dataset.

Abstract: Disclosed embodiments may include a method for bagging ensemble classifiers for imbalanced big data. The system may receive user input comprising a number of machine learning base models to generate. The system may generate the machine learning base models based on the user input. Iteratively for each machine learning base model of the machine learning base models until all machine learning base models are trained, the system may: determine a chunk for a machine learning base model of the machine learning base models, wherein the chunk comprises all minority cases from training data and a plurality of majority cases from the training data and train the machine learning base model with the chunk.

Abstract: A method includes obtaining a first dataset comprising a first set of features and generating a second set of features based on the first set of features set by providing the first dataset to feature primitive stacks that respectively corresponds to features of the second set of features. The method further includes determining a reduced feature set based on the second set of features and a count of correlation values between features of the second set of features, wherein the correlation values satisfy a correlation threshold. The method further includes storing the reduced feature set in a database in association with the first set of features based on a determination that a second dataset comprising the reduced feature set satisfies a set of criteria.

Abstract: A method includes obtaining a first dataset including a first feature set, generating a first set of feature values by providing the first dataset to a set of feature primitive stacks, and determining a reduced set of feature values based on the first set of feature values by dimensionally reducing features of the first set of feature values. The method further includes generating an intermediate set of feature values by providing a value of the first dataset and a value of the reduced set of feature values to at least one feature primitive of the set of feature primitive stacks. The method further includes updating the reduced set of feature values by dimensionally reducing features of the intermediate set of feature values and storing a second dataset including features of the intermediate set of feature values in association with the first feature set.