Model-Interpretability
Model interpretability refers to the degree to which a human can understand the reasons behind a model’s decisions or predictions. It is crucial in machine learning, as it allows users to trust and validate the outcomes generated by models. High interpretability means that the relationships between input features and predictions are clear and comprehensible. This concept is essential for ensuring accountability, especially in high-stakes applications like healthcare and finance, where understanding the rationale behind decisions can significantly impact outcomes. Various models, such as linear regression and decision trees, are inherently more interpretable than complex models like deep neural networks.
Interpretable Models
The easiest way to achieve interpretability is to use only a subset of algorithms that create interpretable models. Linear regression, logistic regression and the decision tree are commonly used inter...
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Exploring Methods for Model-Agnostic Interpretation
Part of building trust in your model comes down to simply understanding the way it works. Interpretability allows us to see model results and why predictions were made. Often times, these aspects can…...
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Unavoidability of Model Interpretability
High score model doesn’t mean that it is interpretable, and worse than that, model results could be misleading. Never trust a model that is telling 99% accuracy at the first shot. Tools like LIME…
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How to Increase the Interpretability of Your Predictive Model
Accuracy and interpretability are said to be diametrically different. Complex models tend to achieve the highest accuracies, while simpler models tend to be more interpretable. But what if we want to…...
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Interpretability
It is difficult to (mathematically) define interpretability. A (non-mathematical) definition of interpretability that I like by Miller (2017) 3 is: Interpretability is the degree to which a human can ...
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InterpretML: Another Way to Explain Your Model
Interpretability can be crucial when implementing ML models. By interpreting models , customers can gain trust in the model and facilitate adoption. It may also be helpful in debugging your model…
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Interpretability and Performance in a Single Model
Machine learning is a discipline full of frictions and tradeoffs but none more important like the balance between accuracy and interpretability. In principle, highly accurate machine learning models…
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Other Interpretable Models
The list of interpretable models is constantly growing and of unknown size. It includes simple models such as linear models, decision trees and naive Bayes, but also more complex ones that combine or ...
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Importance of Interpretability
If a machine learning model performs well, why do we not just trust the model and ignore why it made a certain decision? “The problem is that a single metric, such as classification accuracy, is an in...
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Introduction to Machine Learning Model Interpretation
Regardless of what problem you are solving an interpretable model will always be preferred because both the end-user and your boss/co-workers can understand what your model is really doing. Model…
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Interpretability of Deep Learning Models
Model Interpretability of Deep Neural Networks (DNN) has always been a limiting factor for use cases requiring explanations of the features involved in modelling and such is the case for many…
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Which models are interpretable?
A brief overview of some interpretable machine learning models Continue reading on Towards Data Science
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