Timeline for Reconstituting estimated/predicted values to original scale from MinMaxScaler
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 20, 2019 at 18:52 | comment | added | Anthony from Sydney | Dear 'serali' thank you again. I did the reshuffling of the x and f(x) and made a model on that basis. The result was a better fit. When it came to plotting the data, I plotted dots instead of a lineplot in order to avoid a zig-zag pattern on the plot. In addition I sorted/unshuffled the data back to produce a list of x, f(x) and yhat. Thank you again, Anthony of Sydney | |
| Nov 15, 2019 at 5:08 | comment | added | Anthony from Sydney | Thank you for your post comment and thanks to the moderator for editing and deleting. | |
| Nov 14, 2019 at 7:27 | comment | added | serali | Even before k-fold validation, just a simple shuffling of your dataset once before training will probably improve your results. But you seem to use your entire dataset both for test and for training, which generally is not a good idea. | |
| Nov 14, 2019 at 1:32 | comment | added | Anthony from Sydney | Thank you for your reply. I did some further experimentation and added layers and varied the number of neurons between each layer. In essence, I looked at the plot of (x,f(x)) and (x,yhat) and found them to be very close. In addition when experimenting with layers and number of neurons in each layer, I looked at the lower levels of yhat and found them to be very very close to 0, 1, 4, 9, 16 and very little error. Still need to fine tune, and to examine the fit of the model using k-folds shuffling. | |
| Nov 12, 2019 at 14:41 | history | edited | serali | CC BY-SA 4.0 |
edit added: another potential solution to the posed question
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| Nov 12, 2019 at 13:48 | history | answered | serali | CC BY-SA 4.0 |