Validate & Calibrate
To gain trust for your modeling results, it’s important to know how valid are the results and how you calibrate them further.
![Validate](/_next/image//?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fav0nf7qr%2Fproduction%2F9643d3ceb5aef1e76a19bdcfecf80935465f937b-1300x1300.png&w=3840&q=70)
Post modeling: MAPE and R²
The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of the prediction accuracy of a forecasting method in statistics.
R² or R-Squared is a statistical measure in a regression model that determines the proportion of variance in the dependent variable that can be explained by the independent variable. So, R² shows how well the data fit the regression model (the goodness of fit).
The Sellforte platform offers both of these metrics to validate the results
![Validation](/_next/image//?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fav0nf7qr%2Fproduction%2F7b8ed22c2f915f274bdb9d71aab98b4e601b826f-1607x1206.png&w=3840&q=70)
Lift tests
The best way to calibrate the model by yourself is to do lift test studies. You can do these lift test in the major advertising platforms. A lift test means that an ad will be shown to f.e. 90 % of the target audience and 10 % will be shown a blank ad to see how the control group behaves in relation to the other group. Do lift studies in:
- Google ad platform
- Meta ad platform
- TikTok ad platform
- Smartly ad platform
![Lift tests](/_next/image//?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fav0nf7qr%2Fproduction%2F827938eace153b08bf8317696ff5f349bd283282-1390x604.png&w=3840&q=70)