Bayesian Optimization intelligently balances:Review LaterExploration and exploitationL1 and L2 regularizationModel parameters and hyperparametersGrid Search and Random Search
Question
Bayesian Optimization intelligently balances:Review LaterExploration and exploitationL1 and L2 regularizationModel parameters and hyperparametersGrid Search and Random Search
Solution
Bayesian Optimization intelligently balances Exploration and Exploitation.
Here's a step-by-step explanation:
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Exploration: This is the process of investigating different areas of the parameter space to find regions where the objective function (the function we want to optimize) may be high. This is important because it allows the algorithm to avoid getting stuck in local optima.
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Exploitation: This is the process of investigating areas of the parameter space where the objective function is known to be high, based on the data collected so far. This is important because it allows the algorithm to make the most of the information it has already gathered.
Bayesian Optimization balances these
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