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I'm new in machine learning and I am willing to know better what is the difference between biased and unbiased learners? Anyone can provide some examples?

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Biased in the context that you are speaking means, that your model overfits the training data and can not generalize well. It means your model performs very well on your training data, but can not do well on cross-validation and test data. It is customary to say that biased learners memorize the training data which is really true. Biased learners don't learn the data, they fit the data. For understanding the other usages of bias take a look at this question.

There is something that may be worth mentioning. You may have heard people saying that your model has a high-bias problem. It just means that your model can not learn the training data, whilst the biased learners overfits the training data, means fits the training data. The latter can not generalize well because it has fitted the training data, memorized it, the former can not generalize because it has not learnt even the training data so it has not learnt so much and can not generalize.

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Biased Learner


In machine learning, a biased learner is a learning algorithm that consistently makes predictions that are systematically incorrect in some way. This means that the predictions made by a biased learner will be systematically different from the true values of the target variable, and this difference will not be random or arbitrary.

For example, a biased learner might consistently over- or under-estimate the values of the target variable, or it might systematically favor certain values over others. This systematic error in the predictions made by the learner will introduce bias into the model, and it will affect the accuracy and reliability of the predictions made by the model.

One example of a biased learner is a linear regression model that assumes that the target function is linear. If the true target function is nonlinear, then the linear regression model will make systematic errors in its predictions, and it will be a biased learner.

Another example of a biased learner is a k-nearest neighbors algorithm that assumes that the data is uniformly distributed. If the data is not uniformly distributed, then the k-nearest neighbors algorithm may make systematic errors in its predictions, and it will be a biased learner.

Unbiased Learner


In contrast, an unbiased learner is a learning algorithm that makes predictions that are not systematically biased in any way. This means that the predictions made by an unbiased learner will be unbiased with respect to the true values of the target variable.

An unbiased learner will make predictions that are randomly distributed around the true values of the target variable, and the difference between the predicted values and the true values will be due to random error rather than bias. This will ensure that the predictions made by the model are accurate and reliable, and they will not be systematically biased in any way.

Some examples of unbiased learners include decision trees and random forests, which make no assumptions about the data distribution or the functional form of the target function. These algorithms are expected to make accurate predictions on average, without any systematic bias.

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In short, Inductive bias is a bias that the designer put in, so that the machine can predict, if we don't have this bias, then any data that is "biased" or you can say different from the training set cannot be classified.

An unbiased learner cannot predict anything, it requires the new data has the same attributes as one of the training data. Biased learning instead, can predict. For instance, Find-S algorithm can predict any new instance as positive or negative, on the other hand, Candidate-Elimination algorithm also can predict as long as all the hypothesis in Version space is consistent, that means every hypothesis tells you the same result whether the result is positive or negative, but sometimes it will be inconsistent. In this situation, some of your hypothesis tells you that it's positive while others tell you the result is negative. The reason is that the inductive bias of Candidate-Elimination algorithm does not fully represent the hypothesis space fully.

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