Question

Common classes of problems in machine learning is

Answer 1

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Answer 2

Common classes of problems in machine learning are:

• Machine Learning equips organisations with the information they need to make better-informed, data-driven choices faster than they could be using traditional methods. It isn't, however, the mythological, magical procedure that many people imagine it to be.

• There isn't enough training data in Machine Learning. Machine learning is still not there; most algorithms require a large sum of data to perform successfully.

• It contains data of poor quality. If the training data contains many errors, outliers, and noise, the machine learning learner will be unable to determine a correct underlying pattern. As a result, it will underperform.

• It possesses Irrelevant Characteristics. More relevant characteristics must always be included in the training data, as opposed to none relevant features.

• It includes training data that isn't representative. If the model is trained using a training set that isn't representative, it will be biased towards one class or group in its predictions.

• Underfitting and Overfitting are two aspects of machine learning.

• Machine Learning is all about utilising data to improve machines so we don't have to write them manually. If the total of training data is limited, noisy with errors and outliers, or not representative (resulting in bias), contains irrelevant features, and is neither too simple (means underfitting) nor too complex (means overfitting), the trainee will not perform well (means overfitting).