Poor data quality can cause inaccurate analysis and decision-making in information-driven systems. Algorithms for Machine learning (ML) classification have emerged as efficient tools for addressing a wide range of data quality issues by automatically finding and correcting anomalies in datasets. There are various methods and strategies used to apply ML classifiers to tasks such as data purification, outlier identification, missing value imputation, and record linkage. The evaluation criteria and performance analysis methodologies used to measure the efficacy of machine learning models in resolving data quality issues are evolving.
Overview of Machine Learning Classification Techniques
Machine learning classification techniques are critical for recognizing patterns and making projections from input data. Four popular methods are Naive Bayes, Support Vector Machines (SVM), Random Forest, and Neural Networks. Each strategy has unique advantages and disadvantages.