Here are the meanings of these concepts:
Linearity: There must be a linear relationship between the dependent variable and the independent variables.
Scatterplots can show whether there is a linear or curvilinear relationship.
Homoscedasticity: This assumption states that the variance of error terms is similar across the values of the
independent variables. ...
Look a the CV description I just posted at SE CV. In the first sentence there is a link to Kohavi, which explains bootstrap bias, or estimating error as a function of increasing sample size -- which is what you want. There's also a link to my blog "Help my Neural Network Does Not Work."