Grid Searching From Scratch using Python

Grid searching is a method to find the best possible combination of hyper-parameters at which the model achieves the highest accuracy. Before applying Grid Searching on any algorithm, Data is used to divided into training and validation set, a validation set is used to validate the models. A model with all possible combinations of hyperparameters is tested on the validation set to choose the best combination.

Implementation:

Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Same thing we can do with Logistic Regression by using a set of values of learning rate to find the best learning rate at which Logistic Regression achieves the best accuracy.

It has 8 features columns like i.e “Age”, “Glucose” e.t.c, and the target variable “Outcome” for 108 patients. So in this, we will train a Logistic Regression Classifier model to predict the presence of diabetes or not for patients with such information.

Code: Implementation of Grid Searching on Logistic Regression from Scratch

Output:

Available combinations : [(0.1, 100), (0.1, 200), (0.1, 300), (0.1, 400), 
(0.1, 500), (0.2, 100), (0.2, 200), (0.2, 300), (0.2, 400), (0.2, 500), 
(0.3, 100), (0.3, 200), (0.3, 300), (0.3, 400), (0.3, 500), (0.4, 100), 
(0.4, 200), (0.4, 300), (0.4, 400), (0.4, 500), (0.5, 100), (0.5, 200), 
(0.5, 300), (0.5, 400), (0.5, 500), (0.01, 100), (0.01, 200), (0.01, 300),
(0.01, 400), (0.01, 500), (0.02, 100), (0.02, 200), (0.02, 300), (0.02, 400), 
(0.02, 500), (0.03, 100), (0.03, 200), (0.03, 300), (0.03, 400), (0.03, 500), 
(0.04, 100), (0.04, 200), (0.04, 300), (0.04, 400), (0.04, 500), (0.05, 100), 
(0.05, 200), (0.05, 300), (0.05, 400), (0.05, 500)]

Maximum accuracy achieved by our model through grid searching : 60.0

In the above, we applied grid searching on all possible combinations of learning rates and the number of iterations to find the peak of the model at which it achieves the highest accuracy.

Code: Implementation of Grid Searching on Logistic Regression of sklearn

Output:

Maximum accuracy achieved by sklearn model through grid searching : 62.86

Note: Grid Searching plays a vital role in tuning hyperparameters for the mathematically complex models.