Oracle International Corporation

Zahra Zohrevand

Nipun Agarwal

Karoon Rashedi Nia

Tayler Hetherington

Sanjay Jinturkar

Yasha Pushak

Sam Idicula

Onur Kocberber

Herein are techniques for topic modeling and content perturbation that provide machine learning (ML) explainability (MLX) for natural language processing (NLP). A computer hosts an ML model that infers an original inference for each of many text documents that contain many distinct terms. To each text document (TD) is assigned, based on terms in the TD, a topic that contains a subset of the distinct terms. In a perturbed copy of each TD, a perturbed subset of the distinct terms is replaced. For the perturbed copy of each TD, the ML model infers a perturbed inference. For TDs of a topic, the computer detects that a difference between original inferences of the TDs of the topic and perturbed inferences of the TDs of the topic exceeds a threshold. Based on terms in the TDs of the topic, the topic is replaced with multiple, finer-grained new topics. After sufficient topic modeling, a regional explanation of the ML model is generated.

Efficient and accurate regional explanation technique for NLP models

End-to-end explanation techniques, which efficiently explain the behavior (feature importance) of any machine learning model on large tabular datasets, are disclosed. These techniques comprise two down-sampling methods to efficiently select a small set of representative samples of a high-dimensional dataset for explaining a machine learning model by making use of the characteristics of the dataset or of an explainer of a machine learning model to optimize the explanation quality. These techniques significantly improve the explanation speed while maintaining the explanation quality of a full dataset evaluation.

Efficient and scalable computation of global feature importance explanations

A systematic explainer is described herein, which comprises local, model-agnostic, surrogate ML model-based explanation techniques that faithfully explain predictions from any machine learning classifier or regressor. The systematic explainer systematically generates local data samples around a given target data sample, which improves on exhaustive or random data sample generation algorithms. Specifically, using principles of locality and approximation of local decision boundaries, techniques described herein identify a hypersphere (or data sample neighborhood) over which to train the surrogate ML model such that the surrogate ML model produces valuable, high-quality information explaining data samples in the neighborhood of the target data sample. Combining this systematic local data sample generation and a supervised neighborhood selection approach to weighting generated data samples relative to the target data sample achieves high explanation fidelity, locality, and repeatability when generating explanations for specific predictions from a given model.

Systematic approach for explaining machine learning predictions

A model-agnostic global explainer for textual data processing (NLP) machine learning (ML) models, 'NLP-MLX', is described herein. NLP-MLX explains global behavior of arbitrary NLP ML models by identifying globally-important tokens within a textual dataset containing text data. NLP-MLX accommodates any arbitrary combination of training dataset pre-processing operations used by the NLP ML model. NLP-MLX includes four main stages. A Text Analysis stage converts text in documents of a target dataset into tokens. A Token Extraction stage uses pre-processing techniques to efficiently pre-filter the complete list of tokens into a smaller set of candidate important tokens. A Perturbation Generation stage perturbs tokens within documents of the dataset to help evaluate the effect of different tokens, and combinations of tokens, on the model's predictions. Finally, a Token Evaluation stage uses the ML model and perturbed documents to evaluate the impact of each candidate token relative to predictions for the original documents.

Global, model-agnostic machine learning explanation technique for textual data

Techniques are described for fast approximate conditional sampling by randomly sampling a dataset and then performing a nearest neighbor search on the pre-sampled dataset to reduce the data over which the nearest neighbor search must be performed and, according to an embodiment, to effectively reduce the number of nearest neighbors that are to be found within the random sample. Furthermore, KD-Tree-based stratified sampling is used to generate a representative sample of a dataset. KD-Tree-based stratified sampling may be used to identify the random sample for fast approximate conditional sampling, which reduces variance in the resulting data sample. As such, using KD-Tree-based stratified sampling to generate the random sample for fast approximate conditional sampling ensures that any nearest neighbor selected, for a target data instance, from the random sample is likely to be among the nearest neighbors of the target data instance within the unsampled dataset.

Fast, approximate conditional distribution sampling

Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.

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Vancouver, Canada

Zahra Zohrevand