Drawback of gridsearch cv
- Computationally expensive: GridSearchCV searches for all combinations of hyperparameters in the grid. Therefore, it can be considered expensive, especially when the search area is large or samples are used.
- Comprehensive Search: GridSearchCV performs a comprehensive search on the grid parameter. This means that it evaluates all connections, even if some of them do not appear to improve performance standards. This may cause data loss.
- Not effective for large search space: When dealing with large search space or large number of hyperparameters, GridSearchCV does not work to scale due to large number of connections.
- Limited Exploration: GridSearchCV may not be able to explore the hyperparameter space like other search methods (such as random search). It does not provide much randomness in the search process and the hyperparameter space may not have an expectation space.
- Scalability Issues: GridSearchCV may not work well with some machine learning algorithms and large datasets. This may be impossible when dealing with big data.
- Will not change the results: GridSearchCV does not update its search based on the results of previous tests. It does not learn from the performance of previous hyperparameter combinations and may waste time on similar combinations or not match.
- Limited parallelization: GridSearchCV can be parallelized to some extent, but not all connections can be calculated at the same time. This limits its performance on multi-core processors or distributed computing environments.
- Does not solve the problem of model selection: GridSearchCV only focuses on hyperparameter modification and does not solve the problem of choosing different models or algorithms. Model selection often involves choosing from different types of machine learning, which GridSearchCV does not always support.
Sklearn | Model Hyper-parameters Tuning
Hyperparameter tuning is the process of finding the optimal values for the hyperparameters of a machine-learning model. Hyperparameters are parameters that control the behaviour of the model but are not learned during training. Hyperparameter tuning is an important step in developing machine learning models because it can significantly improve the model’s performance on new data. However, hyperparameter tuning can be a time-consuming and challenging task. Scikit-learn provides several tools that can help you tune the hyperparameters of your machine-learning models. In this guide, we will provide a comprehensive overview of hyperparameter tuning in Scikit-learn.