14 Tips for Successful Systemic Hyperparameter Exploration with Random Search

Photo by Google DeepMind on Unsplash

Random Search is another hyperparameter tuning technique that, instead of exploring all possible combinations like Grid Search, randomly samples a fixed number of hyperparameter combinations from the specified search space. This method can be more efficient in terms of computation time compared to Grid Search. Below is an example code using Python and scikit-learn for Random Search:

I. Random Search Process

• Define Hyperparameter Search Space: Specify a distribution or a range of values for each hyperparameter.
• Randomly Sample Combinations: Generate a specified number of random combinations from the hyperparameter search space.
• Train-Test Model for Each Combination: For each combination, train and evaluate the model using cross-validation.
• Select the Best Combination: Identify the combination of hyperparameters that yields the best performance.

II. Example Code:

from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from scipy.stats import uniform, reciprocal

# Load the iris dataset
iris = load_iris()
X = iris.data
y…