Closed lipikaramaswamy closed 2 years ago
Sorry for the delay.
The link you put is Wikipedia web traffic dataset. Following is the information about it.
For features, we first do x -> log(1 + x)
transform, and then normalize the data to [-1, 1]
range according to the global min=0
and max=18.02413958371087
(i.e., data -> data / max * 2 - 1
). You can do the inverse transform accordingly.
For the three attributes (domain, access type, agent), the meaning for the indexes (i.e., 0 ~ 8 for domain, 0 ~ 2 for access type, and 0 ~ 1 for agent) are:
Projects: ['commons.wikimedia.org', 'de.wikipedia.org', 'en.wikipedia.org', 'es.wikipedia.org', 'fr.wikipedia.org', 'ja.wikipedia.org', 'ru.wikipedia.org', 'www.mediawiki.org', 'zh.wikipedia.org']
Access: ['all-access', 'desktop', 'mobile-web']
Agents: ['all-agents', 'spider']
Thank you! Appreciate the detail.
Hello, I see the input datasets you've shared in drive have been normalized. Would you be able to provide the min/max used scaling for each feature and attribute so an inverse transform can be applied to get samples back in the original scale? Thanks!