New real content in post CP

Author: agarnung

_posts/2025-01-30-conformal-prediction.md

@@ -89,9 +89,7 @@ For an updated use case, see [8]. This method allows us to quantify and communic
 
 The approach is flexible, model-agnostic, and works across various scenarios. It uses calibration data, non-conformity measures, and prediction intervals to ensure reliable predictions, with specified confidence levels (e.g., 95%). However, challenges include ensuring proper calibration across different datasets and accounting for the assumption of interchangeability in the data.
 
-Conformal prediction is particularly useful in high-stakes fields, offering a clearer understanding of prediction limits and helping make more informed decisions.
-
-Let’s look at two example frameworks...
+Let’s look at two exemplar frameworks...
 
 ### Classification Example
 
@@ -105,7 +103,7 @@ this indicates that the model does not have enough certainty to differentiate be
 
 ### Regression Example
 
-In regression, conformal prediction generates prediction intervals. For example, if the model estimates that the price of a stock will be:
+In regression, conformal prediction generates intrinsic prediction intervals. For example, if the model estimates that the price of a stock will be:
 
 $$
     \hat{Y} = 100
@@ -119,6 +117,12 @@ $$
 
 this means that the true price will fall within this interval with a probability of $$1 - \alpha$$.
 
+Conformal prediction is particularly useful in high-stakes fields, offering a clearer understanding of prediction limits and helping make more informed decisions.
+
+AI is transforming **healthcare** by providing non-invasive methods to assess critical health metrics, such as BMI, blood glucose levels, and other factors linked to heart disease and diabetes. However, traditional AI models often produce single-value predictions that may not fully capture the complexity of individual health risks. CP addresses this limitation by incorporating uncertainty bands, offering doctors a clearer range of potential outcomes. This approach enhances diagnostic accuracy, builds patient trust, and ensures fairer decision-making across diverse populations. Recently, CP has enabled more reliable obesity risk assessments [10], outperforming conventional methods. By analyzing age, height, weight, ethnicity, and education level, the model estimates waist circumference with 95% accuracy—without physical measurement. This advancement could revolutionize obesity care by replacing BMI with a more precise predictor of health risks. As researchers refine the model to include lifestyle factors like diet and exercise, AI-driven healthcare is becoming not just more innovative, but also safer and more equitable.
+
+As of today, this is not the time to reinvent the wheel unless we are willing to dive into a very low-level approach, even if we understand the mathematical intricacies of CP. To get started, we can mention some **libraries** and **tools** (ordered by preference based on what I was able to research in less than half a day) to experiment with everything related to this framework, especially in time series and forecasting. Examples include: CatBoost, XGBoost, LightGBM, Prophet, AutoTS, NBEATs, ARIMA, the Theta Model, AutoTS and the Holt-Winters method.
+
 # Conclusion
 
 Conformal prediction provides an accurate approach to measuring uncertainty in machine learning models, offering a confidence range based on past experience. This approach ensures that the real value has a high probability of falling within this range, making the prediction robust and reliable. While it can be computationally expensive in some cases, its variants allow it to be applied in practical situations.
@@ -142,3 +146,5 @@ Conformal prediction provides an accurate approach to measuring uncertainty in m
 [8] [Conformal Prediction: An introduction to measuring uncertainty](https://www.bbvaaifactory.com/conformal-prediction-an-introduction-to-measuring-uncertainty/)
 
 [9] [Valeriy Manokhin's course](https://maven.com/valeriy-manokhin/applied-conformal-prediction)
+
+[10] [Catherine Graham's article](https://hub.jhu.edu/2024/10/01/waistline-ai-bmi/)