Gfyuki

0 $begingroup$ I am running a grid search for identifying the right set of params for Seasonal ARIMA, for over a 1300 training set and range for all the params being 0,1 and 2. But this process is taking over 15minutes. Is that general case with everyone? machine-learning time-series anomaly-detection forecasting grid-search share | improve this question asked 34 mins ago Sandeep Chowdary Sandeep Chowdary 1 New contributor Sandeep Chowdary is a new contributor to this site. Take

0 $begingroup$ When do we use one or the other? My use case: I want to evaluate a linear space to see how good retrieval results are. I have a set of data X (m x n) and some weights W (m x 1). I want to measure the nearest neighbour retrieval performance on W'X with a ground truth value Y. This is a continuous value, so I can't use simple precision/recall. If I use rank correlation, I will find the correlation between retrieved Ys and retrieval rank. If I use nDCG, I will use sorted Y to compute IDCG. I would like to compare this to the correlation value I get when I change Y also. (For example, Y could be head pose angle in one case and age in another) ranking learning-to-rank ndcg share