Machine Learning Common Interview Questions and Answers

What is supervised and unsupervised machine learning?

Supervised machine learning is commonly used as the algorithm is already fed as an input and the algorithm is taught from an equipped dataset. The AI is guided steadily to teach itself with the readily available data resources.

Unsupervised machine learning refers to a process where the machine goes blindly into analysing the input whose outcome is necessarily unknown.

What is Naive Bayes classifier?

Naïve Bayes is an extensively used algorithm for classification task. Naïve Bayes classifier is proved to be effective in textual data analysis. This algorithm is a basis for machine learning as it seeks to work on conditional probability to cut through the improbability of a task in advance.

How to choose notable variables while working on a data set?

Removing the correlated variables is the first step before marking the selective variables as correlation hinders uniqueness among the variables. Other important tools such as linear regression, Random Forest and Lasso regression are keys to select variables in a machine learning process.

What is Bias-Variance trade-off in machine learning?

Bias-Variance is a dilemma of minimizing the errors that stems from 2 different sources at a time. While Bias is based on preconceived assumptions in the learning algorithm, Variance measures whence a set of random numbers are spread across from their average value. Trading off in between these two aspects defines the process of machine algorithm.