The “Stay Alert!” competition from Ford challenged competitors to predict whether a car driver was not alert based on various measured features.
The training data was broken into 500 trials, each trial consisted of a sequence of approximately 1200 measurements spaced by 0.1 seconds. Each measurement consisted of 30 features; these features were presentedin three sets: physiological (P1…P8), environmental (E1…E11) and vehic-ular (V1…V11). Each feature was presented as a real number. For each measurement we were also told whether the driver was alert or not at thattime (a boolean label called IsAlert). No more information on the features was available.
The test data consisted of 100 similar trials but with the IsAlert label hidden. 30% of this set was used for the leaderboard during the competition and 70% was reserved for the ﬁnal leaderboard. Competitors were invited to submit a real number prediction for each hidden IsAlert label. This realprediction should be convertible to a boolean decision by comparison with a threshold.
The accuracy assessment criteria used was “area under the curve” (AUC). The “curve” is the receiver-operating characteristic (ROC) curve where the true-positive rate is plotted against false-positive rate as this threshold is varied. An AUC value will typically vary between 0.5 (random guessing) and 1 (perfect prediction).
See the full explanation of Inference’s method in the attached PDF.
Originally published at blog.kaggle.com on March 25, 2011.
Inference on winning the Ford Stay Alert competition was originally published in Kaggle Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.