1What is Naïve Bayes used for?
To predict categorical outcomes based on conditional probability.
2What does Naïve Bayes assume about features?
It assumes that all features are independent of each other.
3What is Bayes' theorem based on?
It is based on conditional probability.
4What is a common application of Naïve Bayes?
Filtering junk email.
5What does Naïve Bayes use to classify text?
Word frequencies or word count.
6What is the main advantage of Naïve Bayes?
It processes very fast and can easily be trained on small datasets.
7What is the major disadvantage of Naïve Bayes?
It assumes all variables are independent, which can oversimplify relationships.
8What is conditional independence in Naïve Bayes?
It means that given random variables X, Y, and Z, X is independent of Y given Z.
Simple Linear Regression (Lecture 4):
9What is the main objective of simple linear regression?
To establish a relationship between two variables and predict new values.
10What type of variable does simple linear regression predict?
It predicts a continuous or numerical value.
11What does a straight line in linear regression represent?
It represents the line-of-best-fit that minimizes the overall modeling error.
12What is the dependent variable in simple linear regression?
The variable whose values we want to predict, usually denoted as Y.
13What is the independent variable in simple linear regression?
The variable that explains the output, usually denoted as X.
14What is the role of the regression coefficient in SLR?
It minimizes the total error term to find the best-fit line.
15What is the R-square value used for?
It measures the ability of the model to explain the variability in the predictors.
16What does a p-value less than 0.05 signify in SLR?
It indicates that the predictor variable is statistically significant.
These flashcards should help reinforce key concepts from the lectures!