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When data is scarce, software analytics can make many mistakes. For example,consider learning predictors for open source project health (e.g. the number ofclosed pull requests in twelve months time). The training data for this taskmay be very small (e.g. five years of data, collected every month means just 60rows of training data). The models generated from such tiny data sets can makemany prediction errors.  Those errors can be tamed by a {\em landscape analysis} that selects betterlearner control parameters. Our niSNEAK tool (a)~clusters the data to find thegeneral landscape of the hyperparameters; then (b)~explores a fewrepresentatives from each part of that landscape. niSNEAK is both faster andmore effective than prior state-of-the-art hyperparameter optimizationalgorithms (e.g. FLASH, HYPEROPT, OPTUNA).  The configurations found by niSNEAK have far less error than other methods.For example, for project health indicators such as $C$= number of commits;$I$=number of closed issues, and $R$=number of closed pull requests, niSNEAK's12 month prediction errors are \{I=0\%, R=33\%\,C=47\%\}  Based on the above, we recommend landscape analytics (e.g. niSNEAK)especially when learning from very small data sets. This paper only exploresthe application of niSNEAK to project health. That said, we see nothing inprinciple that prevents the application of this technique to a wider range ofproblems.  To assist other researchers in repeating, improving, or even refuting ourresults, all our scripts and data are available on GitHub athttps://github.com/zxcv123456qwe/niSneak

Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health