A place where the highest RSquared value is found, is the place where the line comes to rest. RSquared represents the amount of variance captured by the virtual linear regression line with respect to the total variance captured by the dataset.

In the context of machine learning, particularly linear regression, the process of finding the optimal parameters (coefficients) for the regression line involves minimizing a cost function. This is typically achieved through an optimization algorithm such as gradient descent.

The linear regression line “stops rotating” or finds an optimal spot when the optimization algorithm converges, meaning it reaches a point where further adjustments to the parameters do not significantly decrease the cost function. In practical terms, this means that the coefficients of the regression line have converged to values where the line best fits the data according to the chosen metric (often mean squared error for linear regression).

Therefore, the correct answer would be: The linear regression line stops rotating or finds an optimal spot when the optimization algorithm converges and the cost function is minimized, indicating that the line is well-fitted to the data.