Python Data Science - Naive Bayes

If we are trying to classify if a person walks to work, or drives to work, we can use Bayes Theorom to get which of the following?

• Probability that the individual walks to work

• Probability that the individual drives to work

• A & B

• None of the above

In our NB equation, do we use the following? Prior Probability, Marginal Likelihood, Posterior Probability

• Yes

• No

In our example because we don't have too much information, how do we calculate P(Walks)

• We take the number of walkers divided by the total number of observations. In our example that is 10/30.

• We take the number of drivers and multiply it by the dependent variable.

• We take the number of walkers and add it to the total number of observations.

• None of the above

Is the following true: Bayes theorem requires independence assumption which are sometimes incorrect, so it is seen as "naive"

• Yes

• No

When we have more than 2 classes is the following statement true? It follows a similar/straight process as it always adds up to 1. If we have 3 classes and classify 1 and it's greater than 50% we can assign that class, but if not we have to calculate for each to assign the classification.

• Yes

• No

Naive Bayes is a\an __________ Algororithm.

• Ensemble:

  Each word can be seen as a week classifier and we combine their results as our final classifier.

• Discriminative Model:

  It cannot be more clear!

• Generative Model :

  It generates the fits probability distributions over the training data

• Conditional:

  We need to calculate the conditional probabilities in this model, as a result if falls in this category.

Is the following true: Bayes theorem requires independence assumption which are sometimes incorrect, so it is seen as "naive"

• Yes

• No

The benefit of Naive Bayes:-

• Naive Bayes is one of the fast and easy ML algorithms to predict a class of data sets.

• It is the most popular choice for text classification problems

• It can be used for Binary as well as Multi-class Classifications

• All of the above

Examples of Naive Bayes Algorithm is/are

• Spam filtration

• Sentimental analysis

• Classifying articles

• All of the above

Which of the following is correct about the Naive Bayes?

• Assumes that all the features in a dataset are independent

• Assumes that all the features in a dataset are equally important

• Both

• All of the above