MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately.

Detailed explanation-2: -Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

Detailed explanation-3: -While supervised learning models tend to be more accurate than unsupervised learning models, they require upfront human intervention to label the data appropriately.

Detailed explanation-4: -The most commonly used Supervised Learning algorithms are decision tree, logistic regression, linear regression, support vector machine.

Detailed explanation-5: -Conclusion: Supervised learning is used to train machines so you can develop predictive data models. They have multiple applications across all domains and industries. It requires expertise to build, scale and update the models.