Does your model beat the baseline?

Every time we train a model we should check if its performance beats some baseline, which is a trivial model that doesn’t take the inputs into account. Comparing our model with a baseline model, we can actually figure out whether it actually learns or not.

Is your dataset imbalanced?

Dealing with unbalanced datasets is always hard for a data scientist. Such datasets can create trouble for our machine learning models if we don’t deal with them properly. So, measuring how much our dataset is unbalanced is important before taking the proper precautions. In this article, I suggest some possible techniques.

When to retrain a machine learning model?

Training a model is a complex process requiring much effort and analysis. Once a model is ready, we know that it won’t be valid forever and that we’ll need to train it again. How can we decide if a model needs to be retrained? There are some techniques that help us.