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A model is transparent "if the processes that extract model parameters from training data and generate labels from testing data can be described and motivated by the approach designer." [ 15 ] Interpretability describes the possibility of comprehending the ML model and presenting the underlying basis for decision-making in a way that is ...
Many model-theoretic properties are preserved under interpretability. For example, if the theory of N is stable and M is interpretable in N , then the theory of M is also stable. Note that in other areas of mathematical logic , the term "interpretation" may refer to a structure , [ 1 ] [ 2 ] rather than being used in the sense defined here.
Interpretability: The use of two features with high kurtosis helps in visually understanding the model's decision-making process. In high-dimensional data like this (28 PCA-transformed features), reducing to two dimensions with the most extreme outliers provides an interpretable representation of the results.
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
NMF can be seen as a two-layer directed graphical model with one layer of observed random variables and one layer of hidden random variables. [47] NMF extends beyond matrices to tensors of arbitrary order. [48] [49] [50] This extension may be viewed as a non-negative counterpart to, e.g., the PARAFAC model.
Lasso-regularized models can be fit using techniques including subgradient methods, least-angle regression (LARS), and proximal gradient methods. Determining the optimal value for the regularization parameter is an important part of ensuring that the model performs well; it is typically chosen using cross-validation .
Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows: Principle of the model-based diagnosis. We have a model that describes the behaviour of the system (or artefact). The model is an abstraction of the behaviour of the system and can be incomplete.