Model Selection: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Model Selection = '''Model Selection''' is the process of choosing the best machine learning model from a set of candidate models based on their performance on a given task. It is a critical step to ensure the selected model generalizes well to new, unseen data. == Why Model Selection is Important == Different algorithms and model configurations may perform differently depending on the dataset and problem. Selecting the right model helps: * Improve prediction accur..." |
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* [[Overfitting]] | * [[Overfitting]] | ||
* [[Underfitting]] | * [[Underfitting]] | ||
* [[Cross | * [[Cross Validation]] | ||
* [[Hyperparameter Tuning]] | * [[Hyperparameter Tuning]] | ||
* [[Evaluation Metrics]] | * [[Evaluation Metrics]] | ||
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model selection in machine learning, how to choose machine learning model, model evaluation and selection, model comparison, cross validation for model selection, machine learning model performance, best machine learning algorithm | model selection in machine learning, how to choose machine learning model, model evaluation and selection, model comparison, cross validation for model selection, machine learning model performance, best machine learning algorithm | ||
[[Category:Artificial Intelligence]] | |||