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How do you balance data mining accuracy with interpretability?

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Data mining is the process of extracting useful patterns and insights from large and complex datasets. However, data mining accuracy and interpretability are often at odds. Accuracy refers to how well a data mining model can predict or classify new data, while interpretability refers to how easy it is to understand and explain the model's logic and results. In this article, you will learn about the trade-off between accuracy and interpretability, and some strategies to balance them in your data mining projects.

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Vikalp Mehta

Digital Strategy & Analytics - Aquisition Marketing
Loshani Sigwadi

Senior Data Specialist | Digital Marketing Specialist | Academic Researcher | Higher Education Educator
Salvatore Nicotra

CIO and Technology Teacher

1 Why accuracy and interpretability matter

Accuracy and interpretability are both important aspects of data mining, but they have different implications and applications. Accuracy is crucial for data mining models that are used for decision making, such as credit scoring, fraud detection, or medical diagnosis. Interpretability is essential for data mining models that are used for knowledge discovery, such as customer segmentation, market analysis, or social network analysis. Additionally, interpretability can help validate, debug, and improve the accuracy of data mining models, as well as enhance the trust and acceptance of the models by users and stakeholders.

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Vikalp Mehta

Digital Strategy & Analytics - Aquisition Marketing
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Based on my experience I would say that interpretability is something which is more desired in modern context as majority of companies are still trying to understand the available data. As an example, it is crucial to identify top reasons causing the churn in consumer funnel on your website than predicting the exact customer who is going to churn. Businesses will move to prediction once they have solidified the understanding from the data (Including quality of data) and ready to move to next stage.

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Salvatore Nicotra

CIO and Technology Teacher
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Interpretability is also fundamental to address ethics aspects including data collection, privacy concerns and reproducibility. To extract knowledge and value and validate results.

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Sarveshwaran Rajagopal

Data Scientist and Trainer (Gen AI & NLP) | Empowered 5000+ Professionals & Students to Excel in Data Science ?? | ?? Speaker and Thought Leader in Data Science ??
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Achieving equilibrium between data mining accuracy and interpretability involves choosing appropriate algorithms, prioritizing relevant features, and utilizing ensemble techniques. Consistent validation and transparent result communication are pivotal. This strategy guarantees precision and clear insights. Notably, prioritizing clear result communication surpasses model accuracy, as conveying findings effectively to stakeholders holds paramount importance. Ultimately, effectively presenting complex results using visualizations without compromising accuracy remains pivotal.

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2 How accuracy and interpretability are related

Accuracy and interpretability are often inversely related, meaning that as one increases, the other decreases. This is because data mining models that are more accurate tend to be more complex and nonlinear, while data mining models that are more interpretable tend to be simpler and linear. For example, neural networks and deep learning are highly accurate but difficult to interpret, while decision trees and linear regression are easy to interpret but less accurate. Therefore, data miners face a trade-off between accuracy and interpretability when choosing and designing data mining models.

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Loshani Sigwadi

Senior Data Specialist | Digital Marketing Specialist | Academic Researcher | Higher Education Educator
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I've discovered and learned that detailing the "how" and "why" of the data mining model's processes improves interpretability. Engaging in open communication and soliciting feedback from stakeholders not only encourages engagement but also contributes to improving the model's accuracy.

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Parth Patel, PhD, MS

Executive Director | Global Head AI | Cloud-based Population Genomics | Real-World Data (EHR Systems) | Data Science | Life Sciences & Healthcare | 2x LinkedIn TopVoice
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In my journey, balancing data mining accuracy with interpretability means embracing complexity and clarity. I choose models like decision trees, prune noise, and engineer features for meaning. Techniques like Partial Dependence Plots and SHAP values enhance understanding while maintaining accuracy. Transparency with stakeholders builds trust. This voyage is about finesse and adaptation, where accuracy and interpretability coexist harmoniously.

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3 How to measure accuracy and interpretability

Accuracy and interpretability are both relative and context-dependent concepts, meaning that they depend on the data, the model, and the task at hand. However, there are some common ways to measure them objectively and quantitatively. Accuracy can be measured by various metrics, such as accuracy rate, error rate, precision, recall, F1-score, ROC curve, or AUC, depending on the type and goal of the data mining task. Interpretability can be measured by various criteria, such as simplicity, transparency, consistency, generality, or causality, depending on the type and goal of the data mining model.

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4 How to balance accuracy and interpretability

Balancing accuracy and interpretability is a case-by-case decision that depends on the data, the model, and the task. However, there are some general strategies to help data miners reach a reasonable balance between accuracy and interpretability. Choosing the right data mining model for the task is one such strategy, as is applying feature selection and dimensionality reduction techniques. Additionally, using ensemble methods and hybrid models can improve accuracy and interpretability respectively. Lastly, evaluating and comparing multiple models can help find the optimal balance between accuracy and interpretability.

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5 How to communicate accuracy and interpretability

Balancing accuracy and interpretability is not only a technical challenge, but also a communication challenge. Data miners need to communicate the accuracy and interpretability of their data mining models to different audiences. To do this, they need to use appropriate methods and tools such as plain language and concrete examples, visual aids and interactive interfaces, metrics and indicators, and feedback and evaluation. For instance, if the data mining model is a classifier, data miners can explain what it does, how it works, and what it predicts using real or hypothetical cases. If the model is a clusterer, they can show what it finds, how it groups, and what it means in graphical ways. If the model is a regressor, they can report what it measures, how it evaluates, and how it compares using numerical values. If the model is a recommender, they can solicit feedback from the audience using surveys or reviews.

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Loshani Sigwadi

Senior Data Specialist | Digital Marketing Specialist | Academic Researcher | Higher Education Educator
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From my experience as an associate lecturer and senior data specialist, I've learned that effectively communicating accuracy and interpretability is a multifaceted process. It necessitates customizing your approach to different audiences and turning difficult technical topics into easily understood insights. Using relevant language and real-world examples may bridge the gap between technical and non-technical stakeholders, establishing a shared understanding of the model's performance.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you balance data mining accuracy with interpretability?

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6

1 Why accuracy and interpretability matter

2 How accuracy and interpretability are related

3 How to measure accuracy and interpretability

4 How to balance accuracy and interpretability

5 How to communicate accuracy and interpretability

6 Here’s what else to consider

Data Mining

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How do you balance data mining accuracy with interpretability?

1

2

3

4

5

6

1 Why accuracy and interpretability matter

2 How accuracy and interpretability are related

3 How to measure accuracy and interpretability

4 How to balance accuracy and interpretability

5 How to communicate accuracy and interpretability

6 Here’s what else to consider

Data Mining

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