Making Decisions with Confidence: Harnessing Deep Learning in Uncertain Situations
Introduction:
One thing we have discovered from our customers is that they often require more than just point predictions to make informed decisions. For example, if they want to predict not only the expected temperature but also the probability for every temperature, traditional machine learning models may not provide this capability. At DataRobot, we are constantly pushing the boundaries to meet the needs of our customers, and in this article, we will explore quantile regression as a powerful approach to modeling under uncertainty. We will also discuss how deep learning models, specifically the transformer architecture, can enhance quantile regression. Stay tuned to learn more about the innovative research happening at DataRobot.
Full Article: Making Decisions with Confidence: Harnessing Deep Learning in Uncertain Situations
Quantile regression is a powerful approach to modeling under uncertainty that allows for the prediction of the probability for every temperature. DataRobot, a leading provider of machine learning models, is now offering this important capability to its customers.
Currently, many machine learning models only provide point predictions, such as a single temperature forecast. However, for a more informed decision-making process, it is often necessary to have access to the full distribution of possible outcomes.
Distributional regression and quantile regression are two methods that enable the estimation of the distribution of the target variable for each prediction. Quantile regression, as the name suggests, estimates a selection of quantiles, which split the values into subsets of a given size.
DataRobot has incorporated quantile regression into its platform, allowing users to easily access the full distribution of predictions. This new feature complements DataRobot’s existing support for class probabilities and prediction intervals.
To demonstrate the capabilities of quantile regression, DataRobot used the deepquantiles Python package and the Bishop-S dataset. The results of the quantile regression model showed that it successfully avoided quantile crossings and accurately separated the random test sample.
Traditionally, deep learning models have been challenging to interpret and require a large amount of data and training time. However, with recent advancements in transformer architecture, deep learning models have become more accessible and suitable for uncertainty modeling.
DataRobot compared two deep learning models, DeepQuantiles and FTTransformer, to the NGBoost model using eight publicly available datasets. The results showed that while NGBoost performed reasonably well, it had some difficulties with specific datasets. DeepQuantiles had stronger performance but also had some shortcomings. FTTransformer demonstrated excellent results on average but had a high variance.
In conclusion, quantile regression is a simple yet effective way to model under uncertainty, and the transformer architecture has proven to be useful for this application. Deep learning models have the potential to enhance DataRobot’s regression models that currently lack this capability. DataRobot continues to innovate and explore new research in the field of machine learning.
About the author:
Stefan Hackmann is a Senior Machine Learning Engineer at DataRobot, working in the Office of the CTO. With a background in mathematics and experience as a developer, quant, and portfolio manager, Stefan is passionate about AI innovation and its practical implications.
Summary: Making Decisions with Confidence: Harnessing Deep Learning in Uncertain Situations
One thing we have learned from our customers is that they need more than just point predictions to make informed decisions. In this article, we explore the concept of distributional predictions and how they can provide a more comprehensive understanding of data. We introduce quantile regression as a powerful approach to modeling under uncertainty and discuss its benefits. Furthermore, we discuss how deep learning models, such as the transformer architecture, can be used for quantile regression. We compare the performance of different models on publicly available datasets and highlight the potential of deep learning to enhance regression models. At DataRobot, we are committed to implementing the latest advancements in AI and continue to explore innovative ways to support our customers.
Frequently Asked Questions:
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