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Model Fitting

Model fitting is the process of adjusting a mathematical model to make its predictions as close as possible to real-world data. Imagine trying to find the best-fitting line through a scatter of points on a graph; you want the line to reflect the overall trend of the data. In model fitting, we use algorithms to tweak the model's parameters so that it accurately captures patterns and relationships in the data. This allows us to make reliable predictions or understand behaviors in various fields, from finance to science, based on the underlying information we have.

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    Model fitting is the process of adjusting a mathematical model to accurately represent data. Imagine you have a set of points on a graph, and you want to draw a line that best captures the overall trend of those points. By tweaking the line's slope and position, you improve its alignment with the data. This adjustment helps the model make predictions or understand relationships within the data. In essence, model fitting seeks to find the best possible representation of underlying patterns in order to enhance our insights and forecasts.