What is model selection in ML?

Model selection in machine learning refers to the process of comparing and evaluating different machine learning models to select the best-performing one for a specific dataset and problem. This process is essential for improving the accuracy and performance of a machine learning model.

Model Comparison

Model selection involves testing and comparing various models, such as decision trees, support vector machines, neural networks, etc., to determine which one performs best on a given dataset.

Hyperparameter Tuning

During model selection, hyperparameters are tuned to optimize the performance of each model. Hyperparameters are parameters that are set before the learning process begins and cannot be directly learned from the data.

Accuracy Improvement

The primary goal of model selection is to improve the accuracy of the machine learning model. By selecting the best model for a specific dataset, we aim to achieve higher accuracy in making predictions.

Performance Optimization

Model selection helps in optimizing the overall performance of a machine learning model by selecting the model that generalizes well on unseen data and minimizes errors in predictions.