Study of the Learning Algorithm for Multivariable Data Analysis in Machine Learning Tasks Under Missing Data

Machine Learning algorithms are very sensitive to missing data, and their results depend on the way data is treated by the algorithms. In this work, we will understand missing data when a variable in a dataset is missing data but cannot be eliminated due to its relevance, or when some records of the dataset are missing the values of some of the variables. In general, these problems can be addressed during data preprocessing, for example, by removing variables with a lot of missing data. Now, our interest is to evaluate the robustness of the LAMDA (Learning Algorithm for Multivariable Data Analysis) algorithm to handle missing data at the variable level. A series of experiments will be carried out exposing the algorithm to cases with different percentages of missing variables, and the results obtained will be analyzed. In addition, the performance of the algorithm will be assessed compared to other machine learning algorithms in the context of classification and clustering problems. to carry out this study, several datasets are used. Multiple copies of each of them have been obtained, subjecting them to various transformations that allow us to study different case studies to test the effectiveness of the proposed algorithm.