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→Proposing Split (Interpretable is not the same as explainable): @Geysirhead added "Proposing Split" to make it clear you're proposing a split |
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[[User:Populationecology|Populationecology]] ([[User talk:Populationecology|talk]]) 15:04, 11 May 2020 (UTC)
== Proposing Split (Interpretable is not the same as explainable) ==
Interpretability and explainability are two related concepts that are often used interchangeably, but they have slightly different meanings in the context of machine learning and artificial intelligence. While both concepts aim to provide understanding and insight into how a machine learning model makes its predictions or decisions, they approach the problem from different perspectives. Interpretability refers to the ability to understand or make sense of the internal workings of a machine learning model. It focuses on understanding the relationships between the input features and the model's output. A model is considered interpretable if its inner workings can be easily understood by a human or if it can be represented in a simple and transparent manner. For example, a linear regression model is highly interpretable because the relationship between the input features and the output is explicitly expressed in the form of coefficients. Explainability, on the other hand, goes beyond interpretability and aims to provide a more comprehensive understanding of the model's behavior by explaining why a particular prediction or decision was made. It focuses on providing human-understandable explanations that can justify or rationalize the model's output. Explainable AI techniques try to answer questions such as "Why did the model make this prediction?" or "What were the key factors that influenced the decision?". The goal is to provide insights into the decision-making process of the model, often through the use of visualization, natural language explanations, or highlighting important features. In summary, interpretability is concerned with understanding the internal mechanics of a model, while explainability is concerned with providing understandable justifications for the model's predictions or decisions. Interpretability focuses on the model itself, while explainability focuses on the output and its reasoning. Both concepts are important in different contexts and have different techniques and tools associated with them [[User:Geysirhead|Geysirhead]] ([[User talk:Geysirhead|talk]]) 11:38, 11 June 2023 (UTC)
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