Characterization and modeling are crucial steps in the design process. The strive for higher performance leads to growing complexity in the models and corresponding experiments. For microwave active devices this already resulted in the transition from linear vector measurements to large-signal ones. Moreover, the number of considered input variables gradually increases, e.g., load-impedances, modulation schemes, multi-bias operation, etc. A large number of input and output variables makes designs of experiments, which are traditional to the microwave discipline, inefficient in terms of information gain versus the number of samples. Therefore, the responsibility for the quality of the experiment lies on the shoulders of the user, and depends mainly on one’s experience. This PhD aims at alleviating the curse of dimensionality in the characterization and modeling of active microwave devices. The experiment evaluation and model extraction is bound by the response surface methodology, which allows to fully automate the procedure. Thus, the requirements for vast user experience are lessened. The efficiency of the design is improved with adaptive sampling, which subsequently generates samples for evaluation based on the already acquired information. First, the set of models accompanied by the mixture of adaptive sampling algorithms is established for the later experiments with response surface methodology. The initial load-pull measurements allowed to identify the main challenges in the application of the methodology, i.e., constraining the space of input variables and high computational cost. These problems are addressed in the course of the PhD thesis, and the corresponding results are assessed by means of microwave measurements and numerical simulations. Finally, some of the methodology’s applications, which were developed throughout the thesis, are described including benchmarks with other modeling techniques.