What are the best ways to display Machine Learning results?

To best display Machine Learning results, use real-time dashboards like Grafana and Prometheus linked to your model's REST API. These dashboards capture and track data, allowing interactive visualization of standard and custom ML metrics. They're pivotal for monitoring performance, recognizing important features, and spotting anomalies. With an intuitive interface, they make ML insights clear and actionable for stakeholders, making them ideal for presentations to any audience.

A problem related to any domain can be solved by applying the best possible technique if the relevant and specific information related to that problem is available systematically. The information can be available in various data types or datasets such as imaging databases, 1-dimensional signal datasets, and multidimensional signal datasets. After analyzing the data correctly, it becomes possible to make efficient techniques for problem-solving. Better data visualization provides a better understanding of extracting relevant information, making initial approximations, comprehending adequate inference to get desired outcomes, framing hypotheses and making efficient strategies to implement a technique to obtain appropriate and reliable results

Effective ML result display methods include plots (line, scatter, bar), interactive visuals (Plotly, D3.js), geospatial maps (choropleth, heatmaps), dimensionality reduction plots (PCA, t-SNE), model performance visuals (ROC, confusion matrix), animated plots (time series, transitions), dashboard tools (Tableau, Power BI), explanatory visuals (feature importance, partial dependence), and text visuals (word clouds, highlighting). Adapt choice to data, problem, and audience, ensuring clear labeling and context for easy interpretation.

Visualizing data is crucial, but common mistakes like cluttered visuals, inappropriate scale, and color misuse can hinder interpretation. Ensure axes are labeled, use a color-blind friendly palette, and provide a legend. Consider the data scale; logarithmic scales can help visualize wide-ranging values. Interactivity enhances engagement; tools like Plotly and Bokeh enable interactive plots. Also, consider dimensionality reduction techniques like t-SNE or PCA for high-dimensional data, but be cautious of the interpretation as they have their limitations. Lastly, always question if the visualization is the most effective way to convey the information.

People will really engage and use the data if they can interact somehow with it. If they can only see and try to absorve it, maybe they won't really go deeper in their analysis. So we can try to make some filters and drill down analysis available in order to help people to try different approaches and interact as much as possible with data. Try to combine this approach and good data visualizations showing relationships and aggregations. It could be an intersting approach.

In addition to what is mentioned in this section of the article, utilizing model interpretation techniques like layer-wise relevance propagation (LRP) for neural networks or decision paths for tree-based models can offer deeper insights into the inner workings of your model. We should Ensure clear documentation of the explanation methods and make them accessible to non-technical stakeholders. This creates a shared understanding of the model's decision-making process and enables informed discussions on its implications.

Explaining your model is key for trust and correct application, and different industries have varying expectations. In finance, regulators require detailed explanations of the model's decision-making to ensure fairness and avoid discrimination, making techniques like SHAP values or LIME essential. In healthcare, the focus is on understanding the model's predictions for patient safety and effective treatment, requiring clear explanations using techniques like partial dependence plots or feature importance. In marketing, the emphasis is on understanding the key features driving the model's predictions for effective marketing strategies, making techniques like feature importance or decision paths particularly useful.

Best way to represent machine learning results is to explain the model and its performance in business language. Let's look at a regression model and 2 ways to present its results. 1) 100 features were used in developing regression model that gives 97 R square for predicting airline ticket with RMSE of 1. 2) We developed a model that predicts airline ticket prices and at worse, the prediction can be off by $1 and most important factor that decides ticket prices are seasonality and prices used by competing airlines in the same route. In the 2nd explanation, what changed is the way we explain the model and model metric. It makes model easier to understand for key decision makers who do not have statistics background.

For neural networks, we can use heatmap to visualize the activations in different layers that show areas of interest or salient features in the image the model looks at while making decisions. For classical models, feature importance can be obtained and ranked, which can then help with identifying the most important ones the model uses to predict.

In the captivating realm of machine learning, success is measured not only by the models you build but also by how they perform on uncharted terrain. To truly understand the effectiveness of your model, you must embark on a journey of evaluation. The process begins with quantifying your model's accuracy, precision, recall, F1-score, and the enchanting ROC curve, tailored to your problem and aspirations. These metrics provide a holistic view of your model's prowess, transcending the realm of mere numbers.

One key point is that by conducting error analysis by examining misclassified instances or cases where the model's predictions diverge from ground truth can provide valuable insights into areas for improvement. Also the model's performance needs to be regularly evaluated as new data becomes available. This ensures its effectiveness in real-world applications.

One metric can be how much better the model is as against existing manual or rule based system. For example, if a company uses rule based approach for converting sales lead to paying customers and currently able to do so for 15% of sales lead. We can measure how well the machine learning model can do the same task. If for example, machine learning can convert 18% customers, we can report model metric as 3% improvement as against rule based system.

Numerical metrics such as accuracy, precision, recall, F1 score, or mean squared error would be the first thing you can present as your model metrics. ROC Curve for classification and actual/predicted plots for regression can be used to visualize the model results. For some scenarios, human evaluation is required(NLP/generative models).

Besides, using the model performance metrics like KS or Gini, it's important to show the stakeholders what's in it for them? For this, a cross tab can be used to show old and new models' risk bands along with the bad rate in each bins. This showcases how the new model is sharper than previous one. Also, bivariate analysis of variables showing population, bad rate and information values in certain bins help to understand any variable giving counterintuitive trends. The whole reporting process with these metrics can be automated in PowerBI to look at historical trends. This will highlight any deviations in a particular time period which can call for further investigations.

Concentrate on significant findings, connections, or unexpected outcomes that add to domain knowledge or shed light on the issue. Link the results to accepted theories, body of knowledge, or real-world applications.

I had a new-found appreciation for slides recently. Using slides to share experiment results, ideas, research notes - whether things end up working out or not. Helps to quickly catching up people to what you've been doing, and create room for them to provide feedback, ideas. Because you are actually presenting what you've done, you need to formulate things out, write them out - which is often helpful for self-sanity-checking. Additionally, there's a record of your research steps - so nothing is lost!

Also integrating visualizations such as charts, graphs, and infographics can enhance the clarity and impact of the presentations, making complex concepts more accessible and compelling for the target audience. Another key aspect is to use the storytelling techniques to create a narrative around the findings. This will further captivate the audience, ensuring the machine learning results translate into actionable decisions.

One thing I really like to do is leveraging examples. I did in the period of 2008-2010 my master thesis in artificial intelligence (at that time this term used to more used) on the topic of lightning classification - basically, the outcome of the thesis was a software that classifies lighting by analysing images as Cloud-to-Air, Ground-to-Cloud, Intracloud, Cloud-to-Cloud, and Others (a group for not well identified type). I recall that was really productive to explain people concepts by this example.

And understanding the potential biases embedded in the training data and addressing them through techniques like fairness audits or debiasing strategies is crucial. Embracing a holistic approach to model evaluation enables transparency and generates greater confidence in the model's real-world applicability.

Testing assumptions is an important step. This is even more important for linear models. In many of the cases, it can lead us towards model improvement. For example, if a linear regression model is not performing well, and we find that it violates autocorrelation assumption by performing Durbin-Watson test. We can add a lag variable to improve the model. In many of the cases, addressing the violated assumptions of linear regression can result a superior performance model.

One way of soliciting feedback from audience is by taking the top features discovered from the model and validating it. If anyone has any comment on top features discovered by the model, especially from people who know how business functions. We can take it for improving our model. For example, if a model is used for predicting heart health, but the model is not using cholesterol level as a feature, and it is raised by a doctor. We need to probe further and ensure that cholesterol is used as a feature. We can use different functional forms of the feature to find which transformation gives us the highest impact feature for predicting heart health. using cholesterol.

Once the model is accepted and deployed in production, it should be constantly monitored for wrong predictions. Depending on how serious the wrong prediction is, we can retrain the model periodically.

In addition to metrics, highlighting the tangible business benefits of your machine learning results is crucial. Use visuals, ROI analysis, and real use cases to demonstrate value. #MachineLearning #BusinessImpact

It's important to remember that model predictions result in real time actions. For instance if a model predicts a high risk customer for loan default with the strategy to do N number of automated reminder calls with vanilla script daily, it can irritate the customer and damage lending institution's reputation. Model developers should keep the human factor in mind. Model should take into account why the customer is not paying? Maybe he's sick or lost his job, etc. The model should take these factors and customise succesive calls with empathy.