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edited Apr 13, 2017 at 12:44

This looks like an "actual vs. predicted" plot, with the x axis showing the predicted value and the y axis showing the actual value.

I think your "polynomial" in really a logistic regression fit, but the idea is the same no matter what the model is.

To construct the actual vs predicted plot given input data A, response data B and a model function f, you plot the line y=x and the points {x,y} = {f(A),B}.

More details:

This looks like an "actual vs. predicted" plot, with the x axis showing the predicted value and the y axis showing the actual value.

I think your "polynomial" in really a logistic regression fit, but the idea is the same no matter what the model is.

To construct the actual vs predicted plot given input data A, response data B and a model function f, you plot the line y=x and the points {x,y} = {f(A),B}.

More details:

This looks like an "actual vs. predicted" plot, with the x axis showing the predicted value and the y axis showing the actual value.

I think your "polynomial" in really a logistic regression fit, but the idea is the same no matter what the model is.

To construct the actual vs predicted plot given input data A, response data B and a model function f, you plot the line y=x and the points {x,y} = {f(A),B}.

More details:

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created Jun 29, 2016 at 19:19

xan

This looks like an "actual vs. predicted" plot, with the x axis showing the predicted value and the y axis showing the actual value.

I think your "polynomial" in really a logistic regression fit, but the idea is the same no matter what the model is.

To construct the actual vs predicted plot given input data A, response data B and a model function f, you plot the line y=x and the points {x,y} = {f(A),B}.

More details: