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To determine P(J∣F,I) the probability Jill Stein spoke the words 'freedom' and 'immigration', we'll apply Bayes' Theorem: P(J∣F,I) =P(J)×P(F∣J)×P(I∣J) / P(F,I) Where: P(J) is the prior probability (the overall likelihood of Jill Stein giving a speech). In our case, P(J)=0.5P(J)=0.5. P(F∣J) and P(I∣J) are the likelihoods. These represent the probabilities of Jill Stein saying the words 'freedom' ..
Bayesian inference is a method of statistical analysis that allows us to update probability estimates as new data arrives. In the realm of Natural Language Processing (NLP), it is often used in spam detection, sentiment analysis, and more. Let's explore the initial steps of preprocessing text data for Bayesian inference. 1. Convert Text to Lowercase: To ensure consistency, we convert all text da..
When working with data in Python, the pandas library is a vital tool. However, a common hiccup new users face is the "NameError" related to its commonly used alias 'pd'. Let's understand and resolve this error. The message "NameError: name 'pd' is not defined" indicates that the pandas library, commonly aliased as "pd", hasn't been imported. The solution is straightforward. You need to ensure th..
In the context of the Naive Bayes classifier, probability normalization plays a vital role, especially when we want our probabilities to reflect the true likelihood of an event occurring in comparison to other events. When predicting class labels using the Naive Bayes formula, we compute the product of feature probabilities for each class. However, these products do not sum up to 1 across classe..
Let's break down the regex pattern \b\w+\b and explain it with examples. 1. \w The \w metacharacter matches any word character, which is equivalent to the character set [a-zA-Z0-9_]. This includes: Uppercase letters: A to Z Lowercase letters: a to z Digits: 0 to 9 Underscore: _ 2. \w+ The + is a quantifier that means "one or more" of the preceding character or group. So, \w+ matches one or more ..