What is Happening in the training process when we are fitting a model to the data In any prediction task, the process of “fitting” a model to the data observed in the training process can be best described as...

Assessing all observations available and then backsolving for the dimensionality of best fit.
Making use of all observations available to create a model, then using distance or other measures to assess how well the model characterizes or &quot;fits&quot; its data.
Starting with a linear model and then expanding the dimensionality of that model until it fits the data
Starting with a linear model and then regressing the shape of the line until it fits the data.

These are the four points I am confused about. Which one/ones is/are true?
Options 2 and 4 are correct.
During fitting a model the training process Makes use of all observations available to create a model, then using distance or other measures to assess how well the model characterizes or &quot;fits&quot; its data. Also, Starting with a linear model and then regressing the shape of the line until it fits the data.