Tag Archive for variance

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Diagnosing Learning Algorithms

What happens when our learning algorithm does not predict well? What can we do? The list of possible adjustments is as large as our creativity level but, according to Andrew Ng, we usually end up doing one or more of these actions: Get more training data Get more features Remove some features Fine tune the regularization But which one is the best option for… Read more →

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Bias and Variance

One of the key aspects of understanding prediction models is understanding the prediction error. It measures how good at predicting the model is and a simple way to compute is simply comparing the predicted values against the real observed counterparts (assuming a supervised learning scenario). But the job does not end with calculating the error because this might be large and hence it would… Read more →