# Unbiased Predictor

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## Definition of 'Unbiased Predictor'

An unbiased predictor is a statistical model that does not systematically over- or under-estimate the value of the variable it is trying to predict. In other words, an unbiased predictor is one whose expected value is equal to the true value of the variable being predicted.

There are a number of ways to test whether a predictor is unbiased. One common approach is to use a holdout sample, which is a set of data that is not used to train the model. The model is then used to predict the values of the variable in the holdout sample, and the mean of the predicted values is compared to the true values. If the mean of the predicted values is equal to the true values, then the predictor is unbiased.

Another approach to testing for bias is to use a cross-validation procedure. In cross-validation, the data is divided into a number of folds, and the model is trained on a subset of the data (called the training set) and then tested on the remaining data (called the validation set). This process is repeated for each fold, and the results are averaged to produce an estimate of the model's performance. If the model is unbiased, then the mean of the predicted values in the validation sets should be equal to the true values.

It is important to note that a predictor can be unbiased and still not be very accurate. For example, a predictor that always predicts the mean value of the variable being predicted would be unbiased, but it would not be very accurate. In order to be both unbiased and accurate, a predictor must be able to learn the relationship between the variable being predicted and the other variables in the data.

Unbiased predictors are important in a number of applications, such as forecasting, risk assessment, and medical diagnosis. In these applications, it is important to have a predictor that does not systematically over- or under-estimate the value of the variable being predicted.

There are a number of ways to test whether a predictor is unbiased. One common approach is to use a holdout sample, which is a set of data that is not used to train the model. The model is then used to predict the values of the variable in the holdout sample, and the mean of the predicted values is compared to the true values. If the mean of the predicted values is equal to the true values, then the predictor is unbiased.

Another approach to testing for bias is to use a cross-validation procedure. In cross-validation, the data is divided into a number of folds, and the model is trained on a subset of the data (called the training set) and then tested on the remaining data (called the validation set). This process is repeated for each fold, and the results are averaged to produce an estimate of the model's performance. If the model is unbiased, then the mean of the predicted values in the validation sets should be equal to the true values.

It is important to note that a predictor can be unbiased and still not be very accurate. For example, a predictor that always predicts the mean value of the variable being predicted would be unbiased, but it would not be very accurate. In order to be both unbiased and accurate, a predictor must be able to learn the relationship between the variable being predicted and the other variables in the data.

Unbiased predictors are important in a number of applications, such as forecasting, risk assessment, and medical diagnosis. In these applications, it is important to have a predictor that does not systematically over- or under-estimate the value of the variable being predicted.

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