According to recent performance analyses, MPI collective operations make up a quarter of the execution time on production systems. Machine learning (ML) autotuners use supervised learning to select collective algorithms, significantly improving collective performance. However, we observe two barriers preventing their adoption over the default heuristic-based autotuners. First, a user may find it difficult to compare autotuners because we lack a methodology to quantify their performance. We call this the performance quantification challenge. Second, to obtain the advertised performance, ML model training requires benchmark data from a vast majority of the feature space. Collecting such data regularly on large scale systems consumes far too much time and resources, and this will only get worse with exascale systems. We refer to this as the training data collection challenge. To address these challenges, we contribute (1) a performance evaluation framework to compare and improve collective au-Totuner designs and (2) the Feature scaling, Active learning, Converge, Tune hyperparameters (FACT) approach, a three-part methodology to minimize the training data collection time (and thus maximize practicality at larger scale) without sacrificing accuracy. In the methodology, we first preprocess feature and output values based on domain knowledge. Then, we use active learning to iteratively collect only necessary training data points. Lastly, we perform hyperparameter tuning to further improve model accuracy without any additional data. On a production scale system, our methodology produces a model of equal accuracy using 6.88x less training data collection time.