Naive Bayes Model

Question 1

What is an assumption of Naive Bayes model?

Answer 1

• independence among predictors.

The effect of the value of a predictor variable on a given class is not affected by the values of other predictors.

Question 2

Explain Bayes’ theorem

Answer 2

calculating the posterior probability, which is the likelihood of an event occurring after taking into consideration new information. The weather data set will help you build a model to decide whether to go outside and play soccer.

Question 3

Naive Bayes

Answer 3

calculates posterior probabilities and makes predictions based on which outcome has the highest probability.

Question 4

What type of machine learning model is Naive Bayes model?

Answer 4

supervised learning - classification

Question 5

Bayes’ Theorem equation

Answer 5

equation - find the probability of an event, A, given that another event B is true.

P(A|B) = P(B|A) * P(A) / P(B)

Question 6

P(A)

Answer 6

P(A) probability of the outcome overall. the prior probability of event A before any evidence (feature) is seen.

Question 7

P(B|A)

Answer 7

conditional probability. the probability of B, given A

Question 8

P(B)

Answer 8

probability of the value of the predictor variable

Question 9

In probability, what does “A” represent?

Answer 9

class label: one of the possible outcomes or categories within a dataset.

Question 10

In probability, what does “B” represent?

Answer 10

predictor value

Question 11

What does P(A|B) stand for?

Answer 11

• the posterior probability
• the probability of the class label (A) after the evidence (B, feature) has been seen.

Question 12

In context of probability, what is conditional independence?

Answer 12

Variables B and C are independent of one another on the condition that a third variable, A,assumption that each predictor variable (different Bs in the formula) is independent from the others, conditional on the class (A).

conditional independence is about how variables (Bs) interact with each other when you take into account the influence of a third variable (A).

Question 13

What’s the conditional independence equation?

Answer 13

P(B|C, A) = P(B|A)

the probability of B, given C and A, is equal to the probability of B, given A.

Or given A, introducing C does not change the probability of B.

Question 14

Naive Bayes assumption (in reality)

Answer 14

the predictor variables (B and C) are assumed to be conditionally independent of each other, given the target variable (A).

very often is not actually true.

However, Naive Bayes models still often perform well in spite of the data violating the assumption.

Question 15

Naive Bayes assumption on predictor variables

Answer 15

the individual predictor variables (Bs, Cs) are assumed to contribute equally to the model’s prediction

Question 16

Name 2 Naive Bayes assumption

Answer 16

1. predictor variables are independent of each other
2. predictor variables have equal contribution to model’s prediction

Question 17

Name 3 Advantages of Naive Bayes

Answer 17

1. simplest classification algorithm
2. faster training time
3. highly scalable

Question 18

Naive Bayes Use Cases

Answer 18

1. document analysis/classification
2. spam filtering

Question 19

Name 2 Disadvantages of Naive Bayes

Answer 19

1. Few datasets have truly conditionally independent
2. “zero frequency” problem: when a category or event has not been observed in the training data. This leads to a probability of zero for that category

Question 20

Zero Frequency problem

Answer 20

dataset you’re using has no occurrences of a class label and some value of a predictor variable together. This would mean that there is a probability of zero. Since the final posterior probability is found by multiplying all of the individual probabilities together, the probability of zero would automatically make the result zero.

Question 21

What type of variables are BernoulliNB used for?

Answer 21

Used for binary/Boolean features

Question 22

What type of dataset is CategoricalNB used for? And name a use case?

Answer 22

• Handles categorical features.
• Uses a multinomial distribution to model the probability of each feature value given a class.
  *Specifically designed for categorical data.

Question 23

What is ComplementNB used for?

Answer 23

• Primarily designed to handle imbalanced datasets.
• Calculates the probability of a feature not belonging to a class to improve performance on imbalanced data.
• Typically works with multinomial data (like text)

Question 24

What is GaussianNB used for?

Answer 24

Used for continuous features, normally distributed features

Question 25

What is MultinomialNB used for and what are some use cases?

Answer 25

Used for multinomial (discrete) features

Suitable for: Text classification, document categorization, spam filtering, and other tasks involving count data.

Question 26

What does setting stratify=y mean? When should it be done?

Answer 26

If our master data has a class split of 80/20, stratifying ensures that this proportion is maintained in both the training and test data.

=ytells the function that it should use the class ratio found in theyvariable (our target).

Question 27

When to stratify=y and what are the consequences of not doing it?

Answer 27

• greater your class imbalance, the more important it is to stratify when you split the data.
• If we didn’t stratify, then the function would split the data randomly, and we could get an unlucky split that doesn’t get any of the minority class in the test data, which means we get an ineffective model evaluation.