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This paper describes the NEMO submission to SIGTYP 2020 shared task whichdeals with prediction of linguistic typological features for multiple languagesusing the data derived from World Atlas of Language Structures (WALS). Weemploy frequentist inference to represent correlations between typologicalfeatures and use this representation to train simple multi-class estimatorsthat predict individual features. We describe two submitted ridgeregression-based configurations which ranked second and third overall in theconstrained task. Our best configuration achieved the micro-averaged accuracyscore of 0.66 on 149 test languages.

NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task