  In order to ensure quality and effective learning, fluency, and
comprehension, the proper identification of the difficulty levels of reading
materials should be observed. In this paper, we describe the development of
automatic machine learning-based readability assessment models for educational
Filipino texts using the most diverse set of linguistic features for the
language. Results show that using a Random Forest model obtained a high
performance of 62.7% in terms of accuracy, and 66.1% when using the optimal
combination of feature sets consisting of traditional and syllable
pattern-based predictors.
