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In, hyperparameter optimization or tuning is the problem of choosing a set of optimal for a learning algorithm. The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns. These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined on given independent data. The objective function takes a tuple of hyperparameters and returns the associated loss. Is often used to estimate this generalization performance. Main article: Since grid searching is an exhaustive and therefore potentially expensive method, several alternatives have been proposed.

Serial Key Sketchup Pro 2013 Mac. In particular, a randomized search that simply samples parameter settings a fixed number of times has been found to be more effective in high-dimensional spaces than exhaustive search. This is because oftentimes, it turns out some hyperparameters do not significantly affect the loss.

Therefore, having randomly dispersed data gives more 'textured' data than an exhaustive search over parameters that ultimately do not affect the loss. Bayesian optimization [ ]. Main article: Bayesian optimization is a methodology for the global optimization of noisy black-box functions. Applied to hyperparameter optimization, Bayesian optimization consists of developing a statistical model of the function from hyperparameter values to the objective evaluated on a validation set. Intuitively, the methodology assumes that there is some smooth but noisy function that acts as a mapping from hyperparameters to the objective.

In Bayesian optimization, one aims to gather observations in such a manner as to evaluate the machine learning model the least number of times while revealing as much information as possible about this function and, in particular, the location of the optimum. Bayesian optimization relies on assuming a very general prior over functions which when combined with observed hyperparameter values and corresponding outputs yields a distribution over functions. The methodology proceeds by iteratively picking hyperparameters to observe (experiments to run) in a manner that trades off exploration (hyperparameters for which the outcome is most uncertain) and exploitation (hyperparameters which are expected to have a good outcome). In practice, Bayesian optimization has been shown to obtain better results in fewer experiments than grid search and random search, due to the ability to reason about the quality of experiments before they are run. Gradient-based optimization [ ] For specific learning algorithms, it is possible to compute the gradient with respect to hyperparameters and then optimize the hyperparameters using gradient descent. The first usage of these techniques was focused on neural networks.

Since then, these methods have been extended to other models such as or logistic regression. A different approach in order to obtain a gradient with respect to hyperparameters consists in differentiating the steps of an iterative optimization algorithm using. Evolutionary optimization [ ].