Techniques for Reducing Overfit
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Say I have just trained a model using sklearn and the initial results are that the trainings set had an accuracy of 1.00 and the test set .68. Clearly, the test set overfit. How can I reduce the overfit in my model? I am using decision trees (which are prone to overfitting). Also, is it ok to get 1.00? What is the downside to overfitting beyond unrealistic performance results. If I am just using the test set performance as a benchmark of the model performance do I care if the training set overfit?
machine-learning scikit-learn decision-trees
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Say I have just trained a model using sklearn and the initial results are that the trainings set had an accuracy of 1.00 and the test set .68. Clearly, the test set overfit. How can I reduce the overfit in my model? I am using decision trees (which are prone to overfitting). Also, is it ok to get 1.00? What is the downside to overfitting beyond unrealistic performance results. If I am just using the test set performance as a benchmark of the model performance do I care if the training set overfit?
machine-learning scikit-learn decision-trees
asked 1 min ago
QFIIQFII
15
New contributor
QFII 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 |
$begingroup$
Say I have just trained a model using sklearn and the initial results are that the trainings set had an accuracy of 1.00 and the test set .68. Clearly, the test set overfit. How can I reduce the overfit in my model? I am using decision trees (which are prone to overfitting). Also, is it ok to get 1.00? What is the downside to overfitting beyond unrealistic performance results. If I am just using the test set performance as a benchmark of the model performance do I care if the training set overfit?
machine-learning scikit-learn decision-trees
asked 1 min ago
QFIIQFII
15
New contributor
QFII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Say I have just trained a model using sklearn and the initial results are that the trainings set had an accuracy of 1.00 and the test set .68. Clearly, the test set overfit. How can I reduce the overfit in my model? I am using decision trees (which are prone to overfitting). Also, is it ok to get 1.00? What is the downside to overfitting beyond unrealistic performance results. If I am just using the test set performance as a benchmark of the model performance do I care if the training set overfit?
machine-learning scikit-learn decision-trees
machine-learning scikit-learn decision-trees
asked 1 min ago
QFIIQFII
15
New contributor
QFII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
QFIIQFII
15
New contributor
QFII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
QFIIQFII
15
New contributor
QFII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
QFIIQFII
15
asked 1 min ago
QFIIQFII
15
15
New contributor
QFII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
QFII 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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