Consider a binary classification problem. Suppose I have trained a model on a linearly separable training set, and now I get a new labeled data point which is correctly classified by the model, and far away from the decision boundary. If I now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?
A) When my model is a perceptron.
B) When my model is logistic regression.
C) When my model is an SVM.
D) When my model is Gaussian discriminant analysis.
Answers
Correct answer is (B) and (D)
Answer:
The learned decision boundary is likely to shift in case (B) when the model is logistic regression and case (D) when model is Gaussian discriminant analysis.
Explanation:
What exactly is the Logistic regression?
Logistic regression is a popular Machine Learning method that falls within the supervised learning umbrella.
It is used to forecast the categorical dependent variable from a group of independent factors. The result of the categorical dependent variable is predicted using logistic regression. It can be
- Yes or No
- 0 or 1
- True or False
What exactly is the Gaussian discriminant analysis?
When faced with a classification problem including continuous random variables as input features, we can employ GDA, a generative learning approach that assumes p(xΙy) has a multivariate normal distribution and p(y) has a Bernoulli distribution.
What exactly is a Perceptron?
A perceptron is a component of an artificial neural network. The perceptron is a binary classifier supervised learning system that employs a linear Machine Learning approach.
During preparation, this method allows neurons to learn components and process them one by one.
What exactly is SVM?
Support Vector Machine, or SVM, is a popular supervised learning approach that is used for both classification and regression problems. However, it is largely used in machine learning for classification problems.
The SVM algorithm's purpose is to find the optimal line or decision boundary that can divide n-dimensional space into classes so that we may simply place fresh data points in the proper category in the future.