Model fitting is the process of adjusting a statistical or machine learning model so that it accurately represents the underlying patterns in a dataset. This involves optimizing the model parameters to minimize the difference between the predicted outcomes and the actual observations, often using techniques like least squares or gradient descent. Successful model fitting not only improves predictions but also helps assess the model's complexity and its ability to generalize to unseen data.
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