Efficient Hyperparameter Tuning with Successive Halving

Hyperparameter tuning is an indispensable step in machine learning lifecycle, specifically for model performance. The right hyperparameters can drastically improve model accuracy, generalization to unseen data, and convergence speed. Conversely, poor hyperparameter choices can lead to issues like overfitting, where the model memorizes the training data but performs poorly on new data, or underfitting, where the model is too simplistic to capture underlying data patterns. Some quick examples of hyperparameters would be the <code>learning rate </code>in gradient-based algorithms, or the <code>depth of a tree</code> in decision tree-based algorithms, can directly affect the model’s ability to fit the training data accurately. Regularization hyperparameters, such as <code>L1</code> or <code>L2</code> regularization terms, can help the model generalize better to new data by constraining the complexity of the model. For iterative optimization algorithms like stochastic gradient descent, hyperparameters such as the <code>learning rate </code>and <code>momentum</code> can affect how quickly the model converges to a minimum. Hyperparameters related to model complexity (e.g., depth of a decision tree, <code>number of layers</code> in a neural network) can lead to overfitting if set too high and underfitting if set too low. <a href="https://medium.com/@vireshj/efficient-hyperparameter-tuning-with-successive-halving-7f50a57bb160">Read More</a>