Grid Search: Maximizing Model Performance

Grid Search Maximizing Model Performance

Grid search is one of the well-known techniques used to maximize the implementation of machine learning models. This grid search model falls under the hyperparameter tuning approach. The performance of any machine learning model is measured by the accuracy of its predictions. Model performance is fundamentally generalizing the accuracy of predictions from training data to the real-world database. To get correct results with great accuracy, we need to optimize the machine learning model. This can be executed with the help of the grid search method in Python. We are going to study the Grid search in particular in this article.

Importance of Model Performance in Machine Learning

The model’s performance is an important aspect of the machine learning model because these models are used for various purposes. The different domains are dependent on the predictions of these models. Improved business outcomes, increased revenue, reduced costs, enhanced customer satisfaction, and other benefits can be achieved through high-performance models. When there is a real-world problem, we deal with a large number of data points and need high prediction accuracy to get accurate results. For this purpose, the model’s performance is the most important thing.

Hyperparameter Tuning and Its Impact on Model Accuracy

The data that decides the behavior of the machine learning models is called a hyperparameter. The data scientists pre-decide these hyperparameters while creating the model. The model always is a combination of the different hyperparameters. So, we need to tune those who deliver great results. Optimization of the machine learning model is also a very important thing that can be easily achieved by tuning the hyperparameters.

These hyperparameters also impact the model’s accuracy in many ways, as they impact the learning rate of the model. It determines how fast the model processes a single step during execution. The model should have a moderate learning rate so that it will not overshoot or slow down. The hyperparameters also have control over the number of hidden layers.

These hyperparameters determine the width and depth of hidden layers. So, one should be careful because too many hidden layers in the model lead to overfitting and performing poorly on real-world problems. If there are few or fewer hidden layers in the hidden layers, then the model will ignore the minor patterns of the data points, which leads to underfitting.

The batch size is one more factor that the hyperparameter affects. Batch size is the total number of data points processed by the machine learning model during each iteration. This determines memory consumption. A large batch size consumes more memory as compared to a small one. So, we need to decide the appropriate size of the batch to process the whole model. In the end, it is going to affect accuracy.

Grid Search for Hyperparameter Tuning

Grid search is one of the most famous ways to find the correct set of hyperparameters for a machine learning model. In this technique, all possible combinations of the hyperparameters are tested, and then the best hyperparameters are selected to train the machine learning model. This process is a highly result-delivering technique, so it is very popular in the field of hyperparameter tuning. This process of finding the hyperparameters is known as grid search. Grid search follows some steps to determine the best parameters.

First, we need to define the set of hyperparameters before starting the process. The programmer sets the potential set of tuning values. Then, in the next step, the instance of the machine learning model is set for each hyperparameter. Next, we need to train and validate the model using the determined sets of hyperparameters. After trying all the possible combinations, you can decide the best set of hyperparameters for the model.

Implementation of Grid Search for Hyperparameter Tuning

To implement the grid search as a hyperparameter tuning in the machine learning model, we need to follow the same steps mentioned in the previous section. Let’s try out this with an example in Python.

from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf'],
    'gamma': [0.01, 0.1, 1]
model = SVC()

grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=5)

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2, random_state=42), y_train)

best_params = grid_search.best_params_
best_model = grid_search.best_estimator_

print("Best Hyperparameters:", best_params)
print("Best Model:", best_model)

test_accuracy = best_model.score(X_test, y_test)
print("Test Accuracy:", test_accuracy)

In this example, first we imported the required library, i.e., sklearn. From sklearn, we need to import the GridSearchCV for the searching of grids from the possible values. Then we imported SVC to fit the machine learning model. The iris dataset is loaded for testing and training purposes, and we also require train_test_split from the sklearn for testing and training purposes. Next, we are setting the parameters, i.e., a set of hyperparameters, kernels, and gamma for the model. The model is fitted to an SVC classifier. Then, the function for grid search is applied to the model. The iris dataset is split into training and validation sets. In the end, the results are printed.

Grid Search For Hyperparameter Tuning
Grid Search For Hyperparameter Tuning

In the result, we can clearly see the best hyperparameters are C=1, gamma=0.1, kernel= linear, and the test accuracy is 100%. In this way, you can follow the steps and also find the best hyperparameters for your dataset.

Advantages of Using Grid Search for Hyperparameter Tuning

Grid search is very popular among Python developers for hyperparameter tuning. There are various reasons behind it. The grid search follows the same set of steps/processes to find the best hyperparameters. This makes the whole process way easier as compared to other techniques. This technique is like tried and tested, so every possible combination is tested over the model. So, the overall accuracy and level of the search process are maintained. The results are always the best because it finds the best hyperparameters among all the hyperparameters.

Limitations of Using Grid Search for Hyperparameter Tuning

This grid search technique also comes with a few limitations. The grid search technique is computationally expensive because it is based on the process of trying all possible combinations of hyperparameters, which is not easy for a large number of hyperparameters. The whole process may take an excessive amount of time to process a whole set of hyperparameters. This leads to an increase in the search space for overall hyperparameters. This grid search technique does not deliver higher-accuracy results because of its lack of flexibility.

Best Practices and Practical Tips for Grid Search Technique

There are different ways to overcome the limitations and get the most out of this technique. The first thing is to increase the search space. The search space is a limitation of the grid search technique. If we try to expand this search space by selecting a wide range of possible hyperparameters for the model, then we can overcome this limitation while implementing this technique.

The use of cross-validation while searching for the best set of hyperparameters will ensure the accuracy of the overall machine-learning model in Python. Another practice to make grid search more powerful is to parallelize the grid search so that the large datasets and number of hyperparameters will not affect the overall process and will also be able to maintain great accuracy.


In this article, we have first seen the concept of model performance. The model’s performance plays an important role in the machine learning domain. To maintain that model performance, we need the most suitable set of hyperparameters. Then, we have seen the details of hyperparameters and their influence on the machine-learning models. These hyperparameters can be customized with the help of a technique called grid search. Then, the meaning of grid search and the way to set hyperparameters using grid search are also explained in particular. Then, a simple implementation of the grid search technique is given. After that, we discussed some benefits, restrictions, and best practices for grid search techniques. I hope you will enjoy this article!


Official documentation for sklearn library in Python.