What are the best ways to visualize uncertainty in ML models?

Confidence intervals are a robust tool for visualizing uncertainty in machine learning models. They provide a range of values within which a parameter or prediction is likely to fall. By plotting confidence intervals, you can convey the uncertainty associated with model predictions, helping users make informed decisions based on the level of confidence they desire.

For our class in Machine Learning, we are looking at prediction interval and confidence interval. Prediction interval looks at the average response and the possible range of values it could lie within/between and the confidence interval is the range of values within / between which an estimate could lie.

Error bars are a widely used visualization method for ML model uncertainty. They display the variability of data points or predictions around their mean. These bars, often seen in bar charts and scatterplots, offer a quick and intuitive way to grasp uncertainty levels in your model's output, making them a valuable choice for communicating uncertainty.

Errors bars indicate the variance and the confidence of the estimate. Specifically, it could also be used to detect if the estimate is significant depending on whether it contains the value zero

Density plots are an alternative to violin plots and boxplots to visualize an estimate and observe the manner in which a range of values of a given variable "tend" towards. It is to understand the general tendency of the sample values to possible understand a correlation with the tendency of the same value of the population

Density plots, such as kernel density estimations (KDE) or histograms, provide a visual representation of data distribution. When applied to uncertainty visualization, density plots can reveal the spread and shape of prediction uncertainties. These plots help users understand the likelihood of various outcomes, enabling better decision-making.

Bayesian methods offer a principled approach to quantifying and visualizing uncertainty in ML models. Bayesian techniques, such as Bayesian neural networks or probabilistic programming, explicitly model uncertainty as probability distributions. Visualizing these distributions lets you convey both point estimates and uncertainty intervals, enhancing the transparency of your model.

Conformal Prediction Intervals(PI) can be used to visualize uncertainty in ML models for real-time predictions. PI ranges can act as good guideline for business decision making. PI give an indication of how reliable the predictions are. - If the width of the prediction intervals is broad, then it might be a tough sample & deal with caution. - If the width is narrow, then it's good & confident and so on. Steps to create conformal predictor intervals for Regression: 1. Split the data into Train,Validation & Test 2. Build model on Train and predict on Validation 3. Compute absolute errors in Validation 4. Model that error distribution 5. Error model can be used for calibrating test & real production data to create intervals.

In addition to the core techniques mentioned above, consider incorporating ensemble methods, such as bagging and boosting, to visualize uncertainty. Ensembles provide a way to assess model stability and reliability by aggregating predictions from multiple models. Visualization techniques like stacked bar charts or ensemble prediction intervals can be employed to convey ensemble uncertainty effectively.