Conformal

Explore the concept of conformal predictions applied to the field of time series forecasting and implement it in PythonContinue reading on Towards Data Science » Explore the concept of conformal predictions applied to the field of time series forecasting and implement it in PythonContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the…

Conformal Prediction for Machine Learning Classification —From the Ground UpImplementing conformal prediction for classification without need of bespoke packagesThis blog post is inspired by Chris Molner’s book — Introduction to Conformal Prediction with Python. Chris is brilliant at making new machine learning techniques accessible to others. I’d especially also recommend his books on Explainable Machine Learning.A GitHub repository with the full code may be found here: Conformal Prediction.What is Conformal…

Apply and dynamically expand an interval using backtestingPhoto by Léonard Cotte on UnsplashDepending on the purpose of generating a forecast, evaluating accurate confidence intervals can be a crucial task. Most classic econometric models, built upon assumptions about distributions of predictions and residuals, have a way to do this built in. When moving to machine learning to do time series, such as with XGBoost or recurrent neural networks, it can be more complicated. A popular technique is conformal intervals — a…

Conformal multi-quantile regression with CatboostTexas. Image by Author.In a previous article, we explored the capabilities of Catboost’s multi-quantile loss function, which allows for the prediction of multiple quantiles using a single model. This approach elegantly overcomes one of the limitations of traditional quantile regression, which necessitates the development of a separate model for each quantile, or storing the entire training set in the model. However, there is another disadvantage to quantile regression,…

Using Python and your test set to derive distribution-agnostic intervalsPhoto by Gilly on UnsplashAs important as producing a point estimate for forecasting applications is determining how far off the actual value is likely to be from the prediction. Most forecasts are not 100% accurate so having a good sense of the possibilities when dealing with model implementation becomes crucial. For models with underlying functional forms, such as ARIMA, confidence intervals can be determined using the assumed distribution of the…

Accurate Uncertainty Quantification with MAPIE and TSPIRALPhoto by Lucas George Wendt on UnsplashWhen carrying out a time series forecasting task we are used to developing solutions that produce point-wise estimations of future observations. That’s correct and, if properly validated, they may positively impact business results. Is it possible to do better? Can we provide more detailed forecasts by simply adding further information?Enriching our forecasts with prediction intervals is the key. Practically speaking a…

Part 1 — IntroductionFigure 1: Prediction sets for two different samples and changing coverage rates. As coverage grows, so does the size of the prediction sets. Image by author.A first crucial step towards building trustworthy AI systems is to be transparent about predictive uncertainty. Model parameters are random variables and their values are estimated from noisy data. That inherent stochasticity feeds through to model predictions and should to be addressed, at the very least in order to avoid overconfidence in…

Heatsinks are a staple of PC cooling technology as we know it. Both passive and active coolers make use of heatspreaders and heatsinks, but a team of researchers from the University of Illinois at Urbana-Champaign and the University of California, Berkeley (UC Berkeley) recently found what looks like a far better, all-encompassing, and sleeker solution.The researchers describe their experiments and findings in a paper entitled "High-efficiency cooling via the monolithic integration of copper on electronic devices," as…