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 the example, where we performed sentiment analysis in Chapter 11, Current Trends and the Future of Natural Language Processing where we had an output value (positive/negative) for a given movie review. Though the model output some value between 0 and 1 (0 for negative and 1 for positive) for any sample we input, the model didn't know how uncertain it was about its answer.

Let's understand how uncertainty helps us to make better predictions. For example, a deterministic model might incorrectly say the positivity of the review, I never lost interest, is 0.25 (that is, more likely to be a negative comment). However, a probabilistic model will give a mean value and a standard deviation for the prediction. For example...