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Cost Function
For example, the linear regression algorithm combines a dataset consisting of X and y, the cost function
The cost function typically includes at least one term that causes the learningprocess to perform statistical estimation. The most common cost function is the negative log-likelihood, so that minimizing the cost function causes maximum likelihood estimation.
In simple terms, the cost function is used to evaluate whether the parameter value W is reasonable.
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An engineer is training two different models, Model A and Model B, on the exact same dataset to perform a specific task. The training process aims to find model parameters that minimize a cost function, where a lower value indicates a smaller error between the model's outputs and the desired outputs. After one training iteration, the engineer observes the following:
- Cost for Model A: 2.5
- Cost for Model B: 5.0
Based solely on this information, what is the most logical interpretation of the models' current performance?
Calculating Model Error
An engineer is training a predictive model and plots the value of the cost function at the end of each training iteration. The resulting graph shows a curve that starts at a high value and consistently decreases over many iterations, eventually flattening out at a very low, near-zero value. What does this trend most likely indicate about the training process?