Probability
Next, we will discuss the terminology related to probability theory. Probability theory is a vital part of machine learning, as modeling data with probabilistic models allows us to draw conclusions about how uncertain a model is about some predictions. Consider a use case of sentiment analysis. We want to output a prediction (positive/negative) for a given movie review. Though the model outputs some value between 0 and 1 (0 for negative and 1 for positive) for any sample we input, the model doesn’t know how uncertain it is about its answer.
Let’s understand how uncertainty helps us to make better predictions. For example, a deterministic model (i.e. a model that outputs an exact value instead of a distribution for the value) might incorrectly say the positivity of the review I never lost interest is 0.25 (that is, it’s more likely to be a negative comment). However, a probabilistic model will give a mean value and a standard deviation for the prediction...