Spring has sprung and now it's time to get your tech in gear. Just like your wardrobe needs an update each new season, your devices deserve a refresh too. Cover your iPhone 6 or 6S in a strawberry ice cream sundae that will actually protect your device. The latest Apple Watch bands arrived last week, just in time for spring. In addition to this pretty powder blue, the modern buckle is now available in vibrant marigold, lilac, mint and more.
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions. The prediction sets are also informative in that they correspond to the high density region of the underlying process. While ordinary conformal prediction has high computational cost for functional data, we use the inductive conformal predictor, together with several novel choices of conformity scores, to simplify the computation. Our methods are illustrated on some real data examples.
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Existing methods of calibrating probability estimates are applied globally, ignoring the potential for improvements by applying a more fine-grained model. We propose probability calibration trees, a modification of logistic model trees that identifies regions of the input space in which different probability calibration models are learned to improve performance. We compare probability calibration trees to two widely used calibration methods---isotonic regression and Platt scaling---and show that our method results in lower root mean squared error on average than both methods, for estimates produced by a variety of base learners.
We think it's important for you to understand how we make money. The offers for financial products you see on our platform come from companies who pay us. The money we make helps us give you access to free credit scores and reports and helps us create our other great tools and educational materials. Compensation may factor into how and where products appear on our platform (and in what order). But since we generally make money when you find an offer you like and get, we try to show you offers we think are a good match for you.
Dear Liz: I was recently denied a credit card and told my score was 150 points lower than what my credit reports show. Am I being deceived by the credit reporting agencies? It was such a low number that it's a little hard to believe since I have been approved for other cards recently. Answer: The creditor that denied you should have told you which score it used and from which credit bureau in addition to the actual number. Lenders employ a variety of different scores, but most use some variation of the FICO formula.