Tackling Underfitting And Overfitting Problems In Data Science

One of the major challenges in data science, especially concerning machine learning, is how well the models align themselves to the training data. Underfitting and overfitting are familiar terms while dealing with the problem mentioned above. For the uninitiated, in data science, overfitting simply means that the learning model is far too dependent on training data while underfitting means that the model has a poor relationship with the training data. Ideally, both of these should not exist in models, but they usually are hard to eliminate. Overcoming Overfitting ML experts and statisticians often have different techniques for bringing down overfitting in ML models. The popular ones stand out to be cross-validation and regularisation. These methods are proven to be effective in understa