Parameter grid for hyperparameter tuning.

<p>A grid search with 5-fold cross-validation was executed to identify optimal parameter combinations maximizing model performances. Grid search was conducted in Python 3.11 using GridSearchCV from sklearn.model_selection. To decrease the risk of overfitting, max_depth was limited at 5.</p> <p>(DOCX)</p>

Publication Details

Journal
Figshare
Published
2024-11-26
DOI
https://doi.org/10.1371/journal.pone.0314369.s008
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Parameter grid for hyperparameter tuning.

Figshare
Machine Learning and Data Classification
article

Parameter grid for hyperparameter tuning.

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Abstract

<p>A grid search with 5-fold cross-validation was executed to identify optimal parameter combinations maximizing model performances. Grid search was conducted in Python 3.11 using GridSearchCV from sklearn.model_selection. To decrease the risk of overfitting, max_depth was limited at 5.</p> <p>(DOCX)</p>

Figshare
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Machine Learning and Data Classification
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Parameter grid for hyperparameter tuning. · Figshare (2024) | TGRS Research Map | TGRS