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How do I create a data set that has a set of features for multiple options, with one option being the expected outcome?
The Next CEO of Stack Overflow2019 Community Moderator ElectionStackoverflow API Structure data storageWhich machine learning algorithm should I apply for differentiate question difficulty level with users' resultMore features hurts when underfitting?Gradient Boosting Tree: “the more variable the better”?Gradient boosting vs logistic regression, for boolean featuresXGBoost: predict on only valuable featuresBest ML practice for temporal dependency between featuresWhat approach for creating a multi-classification model based on all categorical features (1 with 5,000 levels)?How do I control for some patients providing multiple samples in my training data?how does XGBoost's exact greedy split finding algorithm determine candidate split values for different feature types?
$begingroup$
Most datasets I see are:
feature 1, feature 2, feature 3, outcome
Where outcome is binary e.g. if they are cancer positive outcome will be 1 and 0 if they don't have cancer.
How do I create a dataset where there are multiple outcomes and each possible outcome has a set of features for it?
e.g. I have a question with 3 possible answers:
"What organ pumps blood around the human body?"
A. Heart
B. Liver
C. Church Organ
And each answer has a set of features with one answer being correct. How would I display this in a csv file? I want to read it into an xgboost algorithm for training.
question, option1 and features, option2 and features, option3 and features, correct option
Many thanks for your help!
machine-learning xgboost
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
$endgroup$
add a comment |
$begingroup$
Most datasets I see are:
feature 1, feature 2, feature 3, outcome
Where outcome is binary e.g. if they are cancer positive outcome will be 1 and 0 if they don't have cancer.
How do I create a dataset where there are multiple outcomes and each possible outcome has a set of features for it?
e.g. I have a question with 3 possible answers:
"What organ pumps blood around the human body?"
A. Heart
B. Liver
C. Church Organ
And each answer has a set of features with one answer being correct. How would I display this in a csv file? I want to read it into an xgboost algorithm for training.
question, option1 and features, option2 and features, option3 and features, correct option
Many thanks for your help!
machine-learning xgboost
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
$endgroup$
add a comment |
$begingroup$
Most datasets I see are:
feature 1, feature 2, feature 3, outcome
Where outcome is binary e.g. if they are cancer positive outcome will be 1 and 0 if they don't have cancer.
How do I create a dataset where there are multiple outcomes and each possible outcome has a set of features for it?
e.g. I have a question with 3 possible answers:
"What organ pumps blood around the human body?"
A. Heart
B. Liver
C. Church Organ
And each answer has a set of features with one answer being correct. How would I display this in a csv file? I want to read it into an xgboost algorithm for training.
question, option1 and features, option2 and features, option3 and features, correct option
Many thanks for your help!
machine-learning xgboost
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
$endgroup$
Most datasets I see are:
feature 1, feature 2, feature 3, outcome
Where outcome is binary e.g. if they are cancer positive outcome will be 1 and 0 if they don't have cancer.
How do I create a dataset where there are multiple outcomes and each possible outcome has a set of features for it?
e.g. I have a question with 3 possible answers:
"What organ pumps blood around the human body?"
A. Heart
B. Liver
C. Church Organ
And each answer has a set of features with one answer being correct. How would I display this in a csv file? I want to read it into an xgboost algorithm for training.
question, option1 and features, option2 and features, option3 and features, correct option
Many thanks for your help!
machine-learning xgboost
machine-learning xgboost
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
asked Mar 24 at 12:23
OultimoCoderOultimoCoder
183
183
add a comment |
add a comment |
1 Answer
1
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oldest
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$begingroup$
The final feature vector would be a concatenation like (for multi-class prediction):
Question google count | option A google count | option B google count | option C google count | option C no. words | option A no. words | other features | label
(1, 2, 3)
There is no need to put features related to option A close to each other (or in any particular order), they just need to be on the same column for all rows regardless of the label.
XGBoost parameters for multi-class classification are:
'objective': 'multi:softprob',
'num_class': 3
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
$endgroup$
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
add a comment |
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1 Answer
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1 Answer
1
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$begingroup$
The final feature vector would be a concatenation like (for multi-class prediction):
Question google count | option A google count | option B google count | option C google count | option C no. words | option A no. words | other features | label
(1, 2, 3)
There is no need to put features related to option A close to each other (or in any particular order), they just need to be on the same column for all rows regardless of the label.
XGBoost parameters for multi-class classification are:
'objective': 'multi:softprob',
'num_class': 3
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
$endgroup$
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
add a comment |
$begingroup$
The final feature vector would be a concatenation like (for multi-class prediction):
Question google count | option A google count | option B google count | option C google count | option C no. words | option A no. words | other features | label
(1, 2, 3)
There is no need to put features related to option A close to each other (or in any particular order), they just need to be on the same column for all rows regardless of the label.
XGBoost parameters for multi-class classification are:
'objective': 'multi:softprob',
'num_class': 3
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
$endgroup$
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
add a comment |
$begingroup$
The final feature vector would be a concatenation like (for multi-class prediction):
Question google count | option A google count | option B google count | option C google count | option C no. words | option A no. words | other features | label
(1, 2, 3)
There is no need to put features related to option A close to each other (or in any particular order), they just need to be on the same column for all rows regardless of the label.
XGBoost parameters for multi-class classification are:
'objective': 'multi:softprob',
'num_class': 3
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
$endgroup$
The final feature vector would be a concatenation like (for multi-class prediction):
Question google count | option A google count | option B google count | option C google count | option C no. words | option A no. words | other features | label
(1, 2, 3)
There is no need to put features related to option A close to each other (or in any particular order), they just need to be on the same column for all rows regardless of the label.
XGBoost parameters for multi-class classification are:
'objective': 'multi:softprob',
'num_class': 3
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
edited Mar 24 at 14:01
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
answered Mar 24 at 12:44
EsmailianEsmailian
2,272218
2,272218
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
add a comment |
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
$begingroup$
Ok, I understand what you're saying, thank you, but let's say one of my additional features is the number of pages returned in a google search when googling the question and answer: e.g. 1,000,000 for option a and question, 200,000 for option b and 0 for option c. How would I add these features to the dataset? Do I just add 3 more rows? optionaresults, optionbresults, optioncresults Because what I don't understand is will the results be attributed to the correct option in the model? If this makes sense?
$endgroup$
– OultimoCoder
Mar 24 at 13:35
1
1
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
$begingroup$
@OultimoCoder updated the example
$endgroup$
– Esmailian
Mar 24 at 13:40
1
1
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
$begingroup$
@Emailian Ahhhh thank you, where I was going wrong was I was assuming the features for each label had to be explicit. I didn't fully understand it. Your edits helped explain it better. I'll wait a day before choosing your answer as the correct one.
$endgroup$
– OultimoCoder
Mar 24 at 13:55
add a comment |
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