Various computational procedures or constraint-based methods for data repairing have been proposed over the last decades to identify errors and, when possible, correct them. However, these approaches have several limitations including the scalability and quality of the values to be used in replacement of the errors. In this paper, we propose a new data repairing approach that is based on maximizing the likelihood of replacement data given the data distribution, which can be modeled using statistical machine learning techniques. This is a novel approach combining machine learning and likelihood methods for cleaning dirty databases by value modification. We develop a quality measure of the repairing updates based on the likelihood benefit and the amount of changes applied to the database. We propose SCARE (SCalable Automatic REpairing), a systematic scalable framework that follows our approach. SCARE relies on a robust mechanism for horizontal data partitioning and a combination of machine learning techniques to predict the set of possible updates. Due to data partitioning, several updates can be predicted for a single record based on local views on each data partition. Therefore, we propose a mechanism to combine the local predictions and obtain accurate final predictions. Finally, we experimentally demonstrate the effectiveness, efficiency, and scalability of our approach on real-world datasets in comparison to recent data cleaning approaches.