Machine learning is tied in with creating predictive models from uncertain data. Uncertainty implies working with imperfect or fragmented information. The main sources of uncertainty in machine learning are noisy data, inadequate coverage of the problem domain, and faulty models.

Probability and Why It Counts?

The investigation of probability is the reason for deciding the degree of confidence we have in expressing that a finding or result is valid. Or then again, better stated, that a result, for example, an average score might not have happened on account of chance alone.

For instance, let us look at Group A which takes interest in 5 hours of additional swim practice every week, and Group B which has no additional swim practice every week.

We find that Group A varies from Group B on a test of strength, however, would we be able to state that the thing that matters is because of the additional training or because of something different?

The instruments that the study of probability gives permit us to decide the specific mathematical likelihood that the thing that matters is because of training as opposed to something different, for example, chance.

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