What are supervised and unsupervised learning models?

Question

Accepted Answer

Supervised learning models are trained on labeled data with known outputs, while unsupervised learning models are trained on unlabeled data without known outputs.

Supervised learning models require labeled data for training, where the algorithm learns to map input data to the correct output.
Examples of supervised learning include linear regression, logistic regression, support vector machines, and neural networks.
Unsupervised learning models do not have labeled output data during training, and the algorithm must find patterns and relationships in the input data.
Examples of unsupervised learning include clustering algorithms like K-means, hierarchical clustering, and dimensionality reduction techniques like PCA.
Semi-supervised learning is a combination of supervised and unsupervised learning, where the model is trained on a small amount of labeled data and a large amount of unlabeled data.

Accepted Answer

Supervised learning relies on labelled data whereas unsupervised learning deals with unlabelled and raw data.

What are supervised and unsupervised learning models?

Popular interview questions of Data Scientist and Machine Learning Engineer

Top HR questions asked in EXL Service Data Scientist and Machine Learning Engineer