In machine learning, selecting the right algorithm is just the first step. The true power of a model lies in fine-tuning it to extract the best performance. This fine-tuning process, known as hyperparameter tuning, is akin to adjusting the dials on a high-performance engine. Get it right, and your model will achieve optimal accuracy and generalization; get it wrong, and you could end up with a model that underperforms or overfits.
Let’s explore hyperparameter tuning across different machine learning algorithms, using a common scenario — predicting house prices. We’ll walk through the tuning process for linear regression, decision trees, and random forests, providing code examples and discussing real-world case studies where hyperparameter tuning made a significant impact.