Classification

Question 1

What is Logistic regression?

Answer 1

It is used when the dependent variable is binary.Logistic regression uses a logistic function, also called the sigmoid function, to map real-valued numbers into values between 0 and 1. The S-shaped curve formed by the logistic function is called the sigmoid function.

It can be used for classification as well as for regression, but mainly it is used for classification.

It is based on the concept of maximum likelihood estimation.

Question 2

What is the Naive Bayes Algorithm?

Answer 2

Naive Bayes is a simple and probabilistic classification algorithm based on Bayes’ theorem. Despite its simplicity, it often performs surprisingly well in various real-world applications, particularly in text classification and spam filtering.

Question 3

What are the steps in Naive Bayes Classification?

Answer 3

1. Find the prior probabilities.
2. Find the conditional probabilities w.r.t each attribute.
3. Fing the probabilities of each class label with each new instance.

Question 4

What is the Gaussian Naive Bayes algorithm?

Answer 4

Gaussian Naive Bayes is a variant of the Naive Bayes algorithm that is specifically designed for data where the features are continuous and assumed to be normally distributed. It is well-suited for classification tasks when dealing with continuous data, and it makes the assumption that the feature values within each class are normally distributed.

Question 5

What are the steps in the Gaussian Naive Bayes Classification?

Answer 5

1. Find the mean and variance of each attribute for each class label.
2. Create a table with the calculated values.
3. Use the Gaussian Distribution equation to find the conditional probabilities for each attribute.
4. Compute the new instance.

Question 6

What is a Decision Tree?

Answer 6

Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree.

Question 7

What is ID3 Algorithm?

Answer 7

It is a decision tree algorithm that uses the information gain to select the most useful attribute for classification.

Question 8

What are the steps involved in the Decision Tree algorithm?

Answer 8

1. Compute the entropy of the entire dataset.
2. Comput the entropy of each attribute.
3. Find the information gain of each attribute.
4. Choose the attribute with the maximum gain and set it as the node of the decision tree.
5. Divide the dataset into subsets.
6. Repeat the above steps until no more datasets can be divided further.