MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Example : I will make a simple example to understand better .

Detailed explanation-2: -Machine Learning is the training of a model from data that generalizes a decision against a performance measure. Training a model suggests training examples. A model suggests state acquired through experience.

Detailed explanation-3: -What a machine does is, it takes a task (T), executes it, and measures its performance (P). Now a machine has a large number of data, so as it processes that data, its experience (E) increases over time, resulting in a higher performance measure (P).

Detailed explanation-4: -Naïve Bayes example code The below example code is to predict whether the person has diabetes or not. In order to predict that we have made use of Gaussian Naïve Bayes algorithm and also to detect the accuracy of our model.

Detailed explanation-5: -Any machine learning problem can be represented as a function of three parameters. Machine Learning Problem = < T, P, E > In the above expression, T stands for the task, P stands for performance and E stands for experience (past data).