Technical Interview Questions
Classification is used when your target is categorical, while regression is used when your target variable is continuous. Both classification and regression belong to the category of supervised machine learning algorithms. Examples of classification problems include: Predicting yes or no Estimating gender Breed of an animal Type of color Examples of regression problems include: Estimating … Read more
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines: If accuracy is a concern, test different algorithms and cross-validate them If the training dataset is small, use models that have low variance and high bias If the training dataset is large, use models that have … Read more
Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step which goes against that goal or in reverse direction, it is penalized. Earlier, … Read more
The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be correct. The algorithm assumes that the presence of one feature of a class is not related to the presence of any other feature (absolute independence of features), given the class variable. For instance, a fruit may be … Read more
Supervised learning – This model learns from the labeled data and makes a future prediction as output Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance. The difference between supervised and unsupervised machine learning lies in the presence or absence of labeled data during … Read more