on ‎2023 Jul 27 12:44 PM
Hello,
I have a question regarding the SAP analytics cloud. More precisely about the classification in the forecast scenarios.
I have created a classification based on a datasheet that looks like this:

SAC does not detect supposed influencers that are obvious and can be detected with the naked eye.
Example here is the day of the week. SAC says that the packages with the days Wednesday and Friday arrive the most on time, but if you go into the datasheet you see that these are the most unpunctual days. The same is true for domestic and international deliveries.

Does anyone have an answer as to why this may be?
Is it due to the uneven distribution of the target variable?
I hope someone can help me with this.
Have a nice day.
Jan
Request clarification before answering.
Jan, you can still improve your predictive power (accuracy) further by bringing more explanatory variables to the dataset. To improve the prediction confidence you need more rows especially from the now famous minority class.
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Hi Antoine,
there are other variables in the data set that are expected to have an impact on the prediction. However, these are not recognized by the SAC.
An example here is whether the package is delivered to the inland or to the foreign country. Here you can see in the dataset that the percentage of packages that are late is significantly higher than if they are sent in the same country.
However, only 500 of the 18000 packages are sent abroad. Is it possible that the SAC does not recognize this?
Hello Jan, difficult to comment w/o seeing this in details. To your point, this behavior might be very specific to these 500 packages, yet not significant at global dataset level. Are you trying to predict packages from being late or explain why they might be late? These are quite different intents. Best regards Antoine
would be interesting then for you to give it a try with Smart Discovery. The main focus of Smart Discovery is to explain, the main focus of Smart Predict is to predict (even if you have some explanatory elements in Smart Predict)
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