Figure 3 shows that the hill climbing strategy applied to our function started at a random hyperparameter value, x=8.4, and then moved toward the function maximum y=0.4 at x=6.9.Once the maximum ...

Before we delve into hyperparameter tuning, let's first understand what hyperparameters are. In machine learning, hyperparameters are parameters that are not learned from the data but are set ...

By Ayo Onikoyi Tuning in Machine Learning Recent benchmarks show that suboptimal hyperparameter choices can slash a model’s accuracy by 20%. This critical insight inspired a comprehensive review ...