Model selection in machine learning involves choosing the best model from a set of models for a particular task.
There are several factors affect the model selection, a few of them are:
Clearly define the problem you are trying to solve and define clearly whether it is classification, regression, or clustering.
Split the dataset into training and testing sets. This ensures that the performance of the model is evaluated on unseen data
Consider models that are commonly used for similar problems or models that are known to perform well on similar datasets.
Compare the performance of the models and select the one that performs best on the testing set.
Tune the hyperparameters of the selected model using techniques such as grid search, random search, etc.
Train the final model on the entire dataset using the tuned hyperparameters and evaluate its performance . If the performance is satisfactory, deploy the model in a production environment.