Common Challenges in Machine Learning and How to Tackle Them

Machine learning continues to become more available daily, and one exciting development is the straightforward availability of machine learning models since data is at the essence of any machine learning problem. Such data is used for the training, validation, and testing of models, and the performance reports of a machine learning model need to be calculated on the independent test data rather than the training or validation tests. Lastly, the data needs to be split so that all three datasets, like training, test, and validation, can have related statistical characteristics. The first crucial step in a standard machine learning workflow after data cleansing is training -- the method of passing training data to a model to learn to identify patterns. After training, the subsequent step is testing, where we examine how the model performs on data outside of the training set. This workflow is known as model evaluation.