How do you explain and interpret the results of predictive analytics based on deep learning?

1 Understand your problem and data

Before you apply any deep learning model to your data, you need to have a clear understanding of the problem you are trying to solve, the data you are working with, and the assumptions and limitations of your approach. You need to know the objective and scope of your analysis, the sources and quality of your data, the features and labels you are using, and the evaluation metrics and benchmarks you are comparing your results with. You also need to be aware of the potential biases, errors, and uncertainties that may affect your data and model.

2 Choose the right deep learning architecture and framework

Depending on the type and complexity of your problem and data, you may need to choose a different deep learning architecture and framework to build and train your model. For example, if you are dealing with images, you may use convolutional neural networks (CNNs) and frameworks such as TensorFlow or PyTorch. If you are dealing with text, you may use recurrent neural networks (RNNs) or transformers and frameworks such as Keras or HuggingFace. You need to explain why you chose a certain architecture and framework, how you configured and optimized your model, and how you validated and tested your model.

3 Visualize and summarize your results

One of the best ways to explain and interpret your results is to use visualizations and summaries that can illustrate the patterns, trends, and insights that your model has learned from the data. For example, you can use graphs, charts, tables, maps, or heatmaps to show the distribution, correlation, or impact of your features and labels, the performance and accuracy of your model, or the predictions and recommendations of your model. You can also use text, captions, or annotations to highlight the key points, findings, or implications of your results.

4 Provide context and comparison

Another way to explain and interpret your results is to provide context and comparison that can show the relevance, significance, and value of your results. For example, you can use historical, current, or future data to show how your results relate to the past, present, or future trends or scenarios. You can also use benchmarks, baselines, or alternatives to show how your results compare to the expected, normal, or optimal outcomes or solutions. You can also use examples, cases, or stories to show how your results apply to the real-world situations or problems.

5 Address the limitations and challenges

No predictive analytics based on deep learning is perfect or complete. There are always limitations and challenges that may affect the validity, reliability, or generalizability of your results. You need to address these issues and explain how you handled or mitigated them. For example, you may face issues such as data scarcity, quality, or privacy, model complexity, overfitting, or interpretability, or ethical, social, or legal implications of your results. You need to be honest, transparent, and humble about the limitations and challenges of your analysis and acknowledge the areas for improvement or further research.

6 Engage your audience and stakeholders

Finally, you need to engage your audience and stakeholders and make sure they understand and appreciate your results. You need to tailor your explanation and interpretation to their needs, interests, and expectations. You need to use simple, clear, and concise language and avoid jargon, acronyms, or technical terms. You need to use stories, analogies, or metaphors to make your results relatable and memorable. You need to use emotions, humor, or curiosity to make your results appealing and persuasive. You need to use questions, feedback, or calls to action to make your results interactive and actionable.

7 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?