The IID Assumption in Model Evaluation
The IID assumption — that data points are independent and identically distributed — is a cornerstone of classical model evaluation in machine learning. When you train and evaluate a model, you typically expect that both your training and test datasets are drawn from the same underlying distribution, and that each data point does not influence the others. This assumption underpins the validity of standard evaluation metrics like accuracy, precision, recall, and mean squared error. If the IID assumption holds, these metrics provide a reliable estimate of how your model will perform on new, unseen data, because the test data is truly representative of the environment in which the model will operate.
Data are considered IID if each sample in the dataset is generated independently of the others (no sample depends on another), and all samples come from the same probability distribution.
When the IID assumption is satisfied, you can use random splits (like train-test or cross-validation) to estimate model performance. This makes offline evaluation (without deploying the model) both feasible and trustworthy.
Consider a scenario where you are building a spam email classifier. You collect a large dataset of emails over a single month, randomly shuffle them, and split them into training and test sets. Since the emails are sampled randomly from the same time period and there is no reason to believe that one email's content affects another's, the IID assumption is likely satisfied. In this case, your offline evaluation — using metrics such as accuracy or F1 score on the test set — will reliably reflect how well your model will perform on future emails from the same source and time frame. This is because the statistical properties of the data remain consistent between training and testing, and each example is independent of the others.
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The IID assumption — that data points are independent and identically distributed — is a cornerstone of classical model evaluation in machine learning. When you train and evaluate a model, you typically expect that both your training and test datasets are drawn from the same underlying distribution, and that each data point does not influence the others. This assumption underpins the validity of standard evaluation metrics like accuracy, precision, recall, and mean squared error. If the IID assumption holds, these metrics provide a reliable estimate of how your model will perform on new, unseen data, because the test data is truly representative of the environment in which the model will operate.
Data are considered IID if each sample in the dataset is generated independently of the others (no sample depends on another), and all samples come from the same probability distribution.
When the IID assumption is satisfied, you can use random splits (like train-test or cross-validation) to estimate model performance. This makes offline evaluation (without deploying the model) both feasible and trustworthy.
Consider a scenario where you are building a spam email classifier. You collect a large dataset of emails over a single month, randomly shuffle them, and split them into training and test sets. Since the emails are sampled randomly from the same time period and there is no reason to believe that one email's content affects another's, the IID assumption is likely satisfied. In this case, your offline evaluation — using metrics such as accuracy or F1 score on the test set — will reliably reflect how well your model will perform on future emails from the same source and time frame. This is because the statistical properties of the data remain consistent between training and testing, and each example is independent of the others.
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