The simple answer is no. Take a look at the following example. It shows a simple linear regression results between two variables $X$ and $Y$. The only difference between the black points and regression line, and the red ones, is that in the second case I changed $Y$ to $Y + 5$. In the "black scenario, the parameters are $\beta_0 = 0.1256, \beta_1 = 4.9122$, while in the "red" scenario $\beta_0 = 5.1256, \beta_1 = 4.9122$. Answer yourself: did the amount of unaccounted biases changed by five between the two scenarios? Is the "black" model worse because it has lower intercept?

Check also When is it ok to remove the intercept in a linear regression model?

I guess that what you are looking for is rather the $R^2$ statistic, but beware that it can be misleading as describes in Is $R^2$ useful or dangerous?

The simple answer is no. Take a look at the following example. It shows a simple linear regression results between two variables $X$ and $Y$. The only difference between the black points and regression line, and the red ones, is that in the second case I changed $Y$ to $Y + 5$. In the "black scenario, the parameters are $\beta_0 = 0.1256, \beta_1 = 4.9122$, while in the "red" scenario $\beta_0 = 5.1256, \beta_1 = 4.9122$. Answer yourself: did the amount of unaccounted biases changed by five between the two scenarios? Is the "black" model worse because it has lower intercept?

The answer to both questions is "no". The only thing that has changed between the two models is that the mean of $Y$ has changed by five, what has led to intercept changing by the same value. That is exactly the role of intercept: to "shift" the regression line upwards, or downwards, on the $y$-axis, so to correct for the mean of the dependent variable. In fact, in most cases we do not expect the intercept to be anything close to zero, because this would mean regression line going through the origin what in many cases leads to inferior models as compared to the models that include the intercept (i.e. it is non-zero).

I guess that what you are looking for is rather the $R^2$ statistic, but beware that it can be misleading as describes in Is $R^2$ useful or dangerous?

The simple answer is no. Take a look at the following example. It shows a simple linear regression results between two variables $X$ and $Y$. The only difference between the black points and regression line, and the red ones, is that in the second case I changed $Y$ to $Y + 5$. In the "black scenario, the parameters are $\beta_0 = 0.1256, \beta_1 = 4.9122$, while in the "red" scenario $\beta_0 = 5.1256, \beta_1 = 4.9122$. Answer yourself: did the amount of unaccounted biases changed by five between the two scenarios?

Check also When is it ok to remove the intercept in a linear regression model?

I guess that what you are looking for is rather the $R^2$ statistic, but beware that it can be misleading as describes in Is $R^2$ useful or dangerous?

The simple answer is no. Take a look at the following example. It shows a simple linear regression results between two variables $X$ and $Y$. The only difference between the black points and regression line, and the red ones, is that in the second case I changed $Y$ to $Y + 5$. In the "black scenario, the parameters are $\beta_0 = 0.1256, \beta_1 = 4.9122$, while in the "red" scenario $\beta_0 = 5.1256, \beta_1 = 4.9122$. Answer yourself: did the amount of unaccounted biases changed by five between the two scenarios? Is the "black" model worse because it has lower intercept?

Check also When is it ok to remove the intercept in a linear regression model?

I guess that what you are looking for is rather the $R^2$ statistic, but beware that it can be misleading as describes in Is $R^2$ useful or dangerous?