Question

Which of the following is NOT a supervised learning algorithm?

a) Linear Regression
b) PCA (Principal Component Analysis)
c) Decision Tree
d) Naive Bayesian

Answer 1

Among the given options, PCA (Principal Component Analysis) is not a supervised learning algorithm. It is an unsupervised learning method used for dimensionality reduction, and differs from supervised techniques like Linear Regression, Decision Trees and Naive Bayesian which require labeled data.

Step-by-step explanation:

The supervised learning algorithms listed are Linear Regression, Decision Tree, and Naive Bayesian. However, PCA (Principal Component Analysis) is not a supervised learning algorithm. It is an unsupervised learning technique used for dimensionality reduction, feature extraction, and data visualization. Supervised algorithms require labeled data to train the model, whereas PCA does not involve any prediction or classification, and it doesn't require a target variable as it works with the input data's intrinsic properties.