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Auto Model Clarifications. Help Please
PyNoob
Hi, I have run the auto model on a dataset of soccer teams and their results. Data is classified as: Event Time (Date Stamp), Completition (Like EPL, Champions League), Home Team (Team playing on the home ground) Home score (Goals scored by the home team), Away Team (Visiting team), Score (Goals scored by the visiting team), Classification (Classifying the outcome of score as a big win or other types as defined).
Where in the Auto model I have run do I get to change these parameters e.g. I want to predict the outcome between Barcelona and Espanyol with Espanyol as the home team?
What does Event time: Half Year = 2 or Event Time:half year = 1 mean? How & Why does it change my output?
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Predictions + Scoring
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Accepted answers
lionelderkrikor
Hi
@PyNoob
,
These attributes are automatically extracted from your date attribute(s) because by default in AutoModel
the option
Extract Date Information
is Enabled and then these attributes are one-hot-encoded.
To recover your original attributes and only your original attributes, you have to set :
- E
xtract date information
to Disabled
-
Remove Columns with too many values
to Disabled (because given that your polynominal attributes have a large number of values, they are removed from the training set.
This way , you can simulate with your original attributes :
Hope this helps,
Regards,
Lionel
All comments
PyNoob
@varunm1
@Telcontar120
@lionelderkrikor
@rfuentealba
@kayman
@SGolbert
@hughesfleming68
@kypexin
@Thomas_Ott
@JEdward
Can you please help me answer my question?
lionelderkrikor
Hi
@PyNoob
,
These attributes are automatically extracted from your date attribute(s) because by default in AutoModel
the option
Extract Date Information
is Enabled and then these attributes are one-hot-encoded.
To recover your original attributes and only your original attributes, you have to set :
- E
xtract date information
to Disabled
-
Remove Columns with too many values
to Disabled (because given that your polynominal attributes have a large number of values, they are removed from the training set.
This way , you can simulate with your original attributes :
Hope this helps,
Regards,
Lionel
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