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## Information

### Error calculation

When training a linear regression model, error calculation measures the disparity between the expected
result and the predicted score. It plays a crucial role in adjusting the model's coefficients and
intercept during the learning process. The error is computed using a formula that subtracts the predicted
score from the expected result.

### Updating coefficients and intercept

Updating coefficients and intercept is a fundamental step in the training of linear regression models.
It involves adjusting these parameters based on the calculated error, learning rate, and input values.
By iteratively updating coefficients and intercept, the model aims to minimize prediction errors and
improve its accuracy over time.

### Learning rate

The learning rate is a hyperparameter that controls how much to change the model in response to the
estimated error each time the model coefficients are updated. A higher learning rate means the model
coefficients will be updated more significantly. Itâ€™s a crucial factor that can affect the speed and
quality of learning. Too high a learning rate can cause the model to converge too quickly to a suboptimal
solution, whereas too low a learning rate can make the training process excessively slow.