This article aims at demystifying what grid search is and how we can use to obtain optimal values of our model parameters. It would be highly beneficial for the reader if the prequels to this article are read to gain a holistic understanding of the various techniques that can be used in optimizing the performance of our machine learning models.
The prequels to this article are :
Dimensionality Reduction Techniques – PCA, Kernel-PCA and LDA Using Python Model Selection and Performance Boosting with k-Fold Cross Validation and XGBoost Grid Search Intuition
For any Data Science problem we can divide