Contents
How to deal with small data sets in machine learning?
Above figure tries to capture the core issues faced while dealing with small data sets and possible approaches and techniques to address them. In this part we will focus on only the techniques used in traditional machine learning and the rest will be discussed in part 2 of the blog.
How does large data affect machine learning performance?
Although traditional Machine learning requires lesser data compared to deep learning, large data impacts the performance in a much similar way. The graph below clearly depicts how the performance of traditional Machine Learning and Deep Learning models improve with large data.
Why is high variance a problem in machine learning?
The major issue with high variance is the model fits the training data really well but it does not generalize well on out of training datasets. This is one of the major reasons validation and test set are very important in the model building process.
When to use upsampling vs under sampling in machine learning?
The choice of Over-sampling vs under-sampling and random vs clustered is determined by business context and data size. Generally upsampling is preferred when the overall data size is small while downsampling is useful when we have a large amount of data.
How to deal with unbalanced data in machine learning?
For example, if we have two classes with data in the ration 4:1, we can apply weights in the ratio 1:4 to the loss function calculation to make the data balanced. This technique helps us easily mitigate the issue of unbalanced data and improves model generalization across different classes.
When to use upsampling or downsampling in machine learning?
Generally upsampling is preferred when the overall data size is small while downsampling is useful when we have a large amount of data. Similarly, random vs clustered sampling is determined by how well the data is distributed. For detailed understanding please refer to the following blog.