Hyperparameter tuning is an essential process in Machine Learning that involves optimizing the settings that guide the training of a Machine Learning model. But these settings themselves are not learned from the data. These settings, called hyperparameters, play a vital role in shaping the behaviour and performance of a Machine Learning model. So, if you want to learn about Hyperparameter Tuning in Machine Learning, this article is for you. In this article, I’ll take you through a guide to Hyperparameter Tuning in Machine Learning and its implementation using Python.
What is Hyperparameter Tuning?
Hyperparameter Tuning is the process of fine-tuning the parameters that are not learned directly during the training of a machine learning model. These parameters govern the behaviour of the training process and significantly impact the model’s performance.
It aims to search through different combinations of hyperparameter values to find the optimal configuration that maximizes the model’s performance on the validation set.
The goal is to strike a balance between underfitting (where the model is too simple to capture the complexity of the data) and overfitting (where the model memorizes the training data but fails to generalize to unseen data).
Hyperparameter Tuning Techniques
There are several techniques for hyperparameter tuning, including:
- Grid Search: Grid search involves defining a grid of possible hyperparameter values and evaluating the model’s performance for all possible combinations within the grid. It can be computationally expensive but exhaustive in searching the hyperparameter space.
- Random Search: Random search randomly samples hyperparameter values from predefined ranges, reducing the computational cost compared to grid search. It explores a more diverse set of hyperparameter combinations and may find good solutions faster.
- Bayesian Optimization: This technique uses probabilistic models to predict the performance of different hyperparameter configurations. It focuses on selecting the most promising set of hyperparameters based on past evaluations, aiming to find the optimal combination more efficiently.
- Gradient-Based Optimization: Particularly useful for neural networks, this technique involves optimizing hyperparameters by treating them as variables in an optimization problem. The goal is to find the combination that minimizes the model’s loss function.
Implementation of Hyperparameter Tuning using Python
Now let’s have a look at the implementation of Hyperparameter tuning using Python by using the Grid Search technique, which involves defining a grid of possible hyperparameter values and evaluating the model’s performance for all possible combinations within the grid:
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
# Load the dataset
iris = load_iris()
X, y = iris.data, iris.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
# Create a Support Vector Machine classifier
svm = SVC()
# Define the hyperparameter grid to search
param_grid = {
'C': [0.1, 1, 10],
'kernel': ['linear', 'rbf', 'poly'],
'gamma': ['scale', 'auto'],
}
# Perform grid search with cross-validation
grid_search = GridSearchCV(svm, param_grid, cv=5)
grid_search.fit(X_train, y_train)
# Print the best hyperparameters and corresponding accuracy
print("Best Hyperparameters:", grid_search.best_params_)
print("Best Accuracy:", grid_search.best_score_)Best Hyperparameters: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
Best Accuracy: 0.9583333333333334
In this example, we used the Iris dataset, splitting it into training and testing sets. Then we performed hyperparameter tuning using a grid search on a Support Vector Machine classifier. The param_grid dictionary defines different values for the hyperparameters C, kernel, and gamma (these are the parameters of the Support Vector Machine algorithm).
So here, the Grid Search algorithm systematically evaluates different combinations of C, kernel, and gamma values, helping us identify the combination that produces the best performance for the SVM model.
Summary
So, Hyperparameter tuning aims to search through different combinations of hyperparameter values to find the optimal configuration that maximizes the model’s performance on the validation set. The goal is to strike a balance between underfitting (where the model is too simple to capture the complexity of the data) and overfitting (where the model memorizes the training data but fails to generalize to unseen data). I hope you liked this article on Hyperparameter Tuning in Machine Learning. Feel free to ask valuable questions in the comments section below.
