In the context of estimating a model, which concept is described as being used to estimate the model's weights and biases?

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Multiple Choice

In the context of estimating a model, which concept is described as being used to estimate the model's weights and biases?

Training a model involves adjusting its weights and biases by minimizing a loss function over labeled examples. This learning happens using data from the training set, which provides the input features and the corresponding targets the model tries to predict. An optimization algorithm (such as gradient descent) uses those examples to update the parameters iteratively until the loss is minimized. The validation set and test set serve different purposes: the validation set helps monitor performance and guide hyperparameter tuning, while the test set measures final generalization. Orthogonality is a geometric property that isn’t how parameters are learned. Therefore, the process of estimating the model's weights and biases relies on the training set.

Training a model involves adjusting its weights and biases by minimizing a loss function over labeled examples. This learning happens using data from the training set, which provides the input features and the corresponding targets the model tries to predict. An optimization algorithm (such as gradient descent) uses those examples to update the parameters iteratively until the loss is minimized. The validation set and test set serve different purposes: the validation set helps monitor performance and guide hyperparameter tuning, while the test set measures final generalization. Orthogonality is a geometric property that isn’t how parameters are learned. Therefore, the process of estimating the model's weights and biases relies on the training set.

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