adjective capable of being understood or explained
Interpretable statistical models are crucial for making inferences and drawing conclusions from data.
In the field of data science, interpretable models are preferred as they allow for better understanding and explanation of the results.
Interpretable AI systems are necessary for ensuring accountability and avoiding bias in decision-making processes.
Interpretable machine learning models are important for ensuring transparency and trust in the decision-making process.
In the context of writing, 'interpretable' may refer to the clarity and coherence of the text, making sure that the message is easily understood by the audience.
Psychologists may use 'interpretable' to describe data or results that can be easily analyzed and understood, allowing for clear interpretations and conclusions to be drawn.
Data scientists may use 'interpretable' to describe machine learning models or algorithms that can be easily explained and understood by non-experts, ensuring transparency and trust in the results.
Legal analysts may use 'interpretable' to refer to laws, regulations, or court rulings that are clear and can be easily understood and applied in different cases or situations.