How to structure a scikit-learn machine learning project for predicting sports outcomes?
$begingroup$
I'm a beginner in machine learning so please bear with what seem like uninformed questions.
I'm trying to create a model that can predict the outcomes of sporting events. My approach to training and testing the model is as follows:
- train the model on last X seasons worth of data
- for the current season, iterate through each game in chronological order (to prevent data leakage):
- predict the outcome of the game (so I can evaluate whether the model gets things right)
- feed that game's data back into the model via the
partial_fit(https://scikit-learn.org/stable/glossary.html#term-partial-fit) method
If I wanted to train/test the model more, I was thinking I could select different seasons as the "test season", but in all situations, I would feed the game data for the tests season in chronological order.
As far as I can tell, the demands of this particular type of problem don't adhere to the typical "train-test" split that I see in all the "intro to machine learning" types of tutorials.
From what I've read, this is an online learning problem because the "test set" data needs to predicted to evaluate the model's accuracy, but it also needs to be fed back into the model so that the model can adjust to trends over the current season.
I'm a bit concerned, though, because there are only two regressors in the entire scikit learn library that support online-learning via the partial_fit method (and they are both linear only models).
Could anyone provide feedback on whether I am approaching this problem properly? If so, is there a workflow I should use within scikit-learn that would allow me to use some more complex models (e.g, random forest or bayes) that would support this type of prediction problem? Thanks!
machine-learning scikit-learn online-learning sports
asked 4 mins ago
Josh RebackJosh Reback
1
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Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I'm a beginner in machine learning so please bear with what seem like uninformed questions.
I'm trying to create a model that can predict the outcomes of sporting events. My approach to training and testing the model is as follows:
- train the model on last X seasons worth of data
- for the current season, iterate through each game in chronological order (to prevent data leakage):
- predict the outcome of the game (so I can evaluate whether the model gets things right)
- feed that game's data back into the model via the
partial_fit(https://scikit-learn.org/stable/glossary.html#term-partial-fit) method
If I wanted to train/test the model more, I was thinking I could select different seasons as the "test season", but in all situations, I would feed the game data for the tests season in chronological order.
As far as I can tell, the demands of this particular type of problem don't adhere to the typical "train-test" split that I see in all the "intro to machine learning" types of tutorials.
From what I've read, this is an online learning problem because the "test set" data needs to predicted to evaluate the model's accuracy, but it also needs to be fed back into the model so that the model can adjust to trends over the current season.
I'm a bit concerned, though, because there are only two regressors in the entire scikit learn library that support online-learning via the partial_fit method (and they are both linear only models).
Could anyone provide feedback on whether I am approaching this problem properly? If so, is there a workflow I should use within scikit-learn that would allow me to use some more complex models (e.g, random forest or bayes) that would support this type of prediction problem? Thanks!
machine-learning scikit-learn online-learning sports
asked 4 mins ago
Josh RebackJosh Reback
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm a beginner in machine learning so please bear with what seem like uninformed questions.
I'm trying to create a model that can predict the outcomes of sporting events. My approach to training and testing the model is as follows:
- train the model on last X seasons worth of data
- for the current season, iterate through each game in chronological order (to prevent data leakage):
- predict the outcome of the game (so I can evaluate whether the model gets things right)
- feed that game's data back into the model via the
partial_fit(https://scikit-learn.org/stable/glossary.html#term-partial-fit) method
If I wanted to train/test the model more, I was thinking I could select different seasons as the "test season", but in all situations, I would feed the game data for the tests season in chronological order.
As far as I can tell, the demands of this particular type of problem don't adhere to the typical "train-test" split that I see in all the "intro to machine learning" types of tutorials.
From what I've read, this is an online learning problem because the "test set" data needs to predicted to evaluate the model's accuracy, but it also needs to be fed back into the model so that the model can adjust to trends over the current season.
I'm a bit concerned, though, because there are only two regressors in the entire scikit learn library that support online-learning via the partial_fit method (and they are both linear only models).
Could anyone provide feedback on whether I am approaching this problem properly? If so, is there a workflow I should use within scikit-learn that would allow me to use some more complex models (e.g, random forest or bayes) that would support this type of prediction problem? Thanks!
machine-learning scikit-learn online-learning sports
asked 4 mins ago
Josh RebackJosh Reback
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm a beginner in machine learning so please bear with what seem like uninformed questions.
I'm trying to create a model that can predict the outcomes of sporting events. My approach to training and testing the model is as follows:
- train the model on last X seasons worth of data
- for the current season, iterate through each game in chronological order (to prevent data leakage):
- predict the outcome of the game (so I can evaluate whether the model gets things right)
- feed that game's data back into the model via the
partial_fit(https://scikit-learn.org/stable/glossary.html#term-partial-fit) method
If I wanted to train/test the model more, I was thinking I could select different seasons as the "test season", but in all situations, I would feed the game data for the tests season in chronological order.
As far as I can tell, the demands of this particular type of problem don't adhere to the typical "train-test" split that I see in all the "intro to machine learning" types of tutorials.
From what I've read, this is an online learning problem because the "test set" data needs to predicted to evaluate the model's accuracy, but it also needs to be fed back into the model so that the model can adjust to trends over the current season.
I'm a bit concerned, though, because there are only two regressors in the entire scikit learn library that support online-learning via the partial_fit method (and they are both linear only models).
Could anyone provide feedback on whether I am approaching this problem properly? If so, is there a workflow I should use within scikit-learn that would allow me to use some more complex models (e.g, random forest or bayes) that would support this type of prediction problem? Thanks!
machine-learning scikit-learn online-learning sports
machine-learning scikit-learn online-learning sports
asked 4 mins ago
Josh RebackJosh Reback
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Josh RebackJosh Reback
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Josh RebackJosh Reback
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Josh RebackJosh Reback
1
asked 4 mins ago
Josh RebackJosh Reback
1
1
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Josh Reback is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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