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There are a few options, but none of them give an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguousModel selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.

There are a few options, but none of them give an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.

There are a few options, but none of them give an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.
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omri374
omri374
  • 46
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There are a few options, but none of them givengive an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.

There are a few options, but none of them given an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.

There are a few options, but none of them give an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.
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omri374
omri374
  • 46
  • 3

There are a few options, but none of them given an "absolute truth" result between 0 and 1. The two common options are AIC and BIC. see here: Model selection with nonlinear fitting? Statistical tests seem ambiguous

Another option is the S measure: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

Some thoughts:

  1. As models become more complex, there are less "absolute truth" answers like R^2 between 0 and 1. With more flexible models, it's easy to get to a 100% fit of training data, and metrics like R^2 are irrelevant.
  2. Make sure you test your model with some test data. non linear models tend to overfit.