The ability to explain or to present an ML models reasoning in understandable terms to a human.
Most linear regression models, for example, are highly interpretable. (You merely need to look at the trained weights for each feature .) Decision forests are also highly interpretable. Some models, however, require sophisticated visualization to become interpretable.
목차 목차 관련 글 Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet 이 글을 인용: “Since then, scaling sparse autoencoders has been a major priority of the Anthropic interpretability team, and w…” TabNet: Attentive Interpretable Tabular Learning 이 글을 인용: “We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.” ML crash course - Linear regression “Models with multiple features have two or more weights, e.g.:” 선형 모델 이 글을 인용: “Linear models are usually easier to train and more interpretable than deep models.” Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams 이 글을 인용: “By identifying associated challenges, such as the limited model interpretability and overfitting the design to …” Introduction to machine learning problem framing “Introduction to Machine Learning Problem Framing is a course provided by Google.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] Philosophy of Science for Machine Learning “Philosophy of Science for Machine Learning is an open access book published by Springer Nature Link.” Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” ML crash course - Logistic regression 관련 절: Loss and regularization Clipping (machine learning) “For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60.” Downsampling (machine learning) “Reducing the amount of information in a feature in order to train a model more efficiently.” 기계학습 “2024-08-24 - What's Really Going On in Machine Learning?” 지도 학습 “Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresp…” Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” Epoch (machine learning) “In many cases, you'll need to experiment with how many epochs it takes for the model to converge.” Generalization (machine learning) “A model's ability to make correct predictions on new, previously unseen data.” Parameter (machine learning) “For example, learning rate is a hyperparameter.” 회귀 모형 “Linear regression, which finds the line that best fits label values to features.” Example (machine learning) “Examples in supervised learning fall into two general categories:”