A data scientist is developing a machine learning model to predict house prices in a competitive real estate market. They initially select a loss function that heavily penalizes large errors, hoping it will improve the model’s performance. However, after training, they observe that the model struggles to converge and produces unstable predictions, with large variations in price predictions for similar houses. The data scientist suspects that the chosen loss function is causing these issues. Why is it crucial to select the right loss function in this situation?
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