Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset. This is called feature selection and there are many different types of algorithms that can be used. It is possible to frame the problem of feature selection as an optimization problem. In the case that […]
Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks. There are perhaps hundreds of popular optimization algorithms, and perhaps tens […]
Deep learning neural network models are fit on training data using the stochastic gradient descent optimization algorithm. Updates to the weights of the model are made, using the backpropagation of error algorithm. The combination of the optimization and weight update algorithm was carefully chosen and is the most efficient approach known to fit neural networks. […]
Genetic Programming (GP) is an algorithm for evolving programs to solve specific well-defined problems. It is a type of automatic programming intended for challenging problems where the task is well defined and solutions can be checked easily at a low cost, although the search space of possible solutions is vast, and there is little intuition […]
Stochastic Hill climbing is an optimization algorithm. It makes use of randomness as part of the search process. This makes the algorithm appropriate for nonlinear objective functions where other local search algorithms do not operate well. It is also a local search algorithm, meaning that it modifies a single solution and searches the relatively local […]
Curve fitting is a type of optimization that finds an optimal set of parameters for a defined function that best fits a given set of observations. Unlike supervised learning, curve fitting requires that you define the function that maps examples of inputs to outputs. The mapping function, also called the basis function can have any […]
Applied machine learning is challenging because the designing of a perfect learning system for a given problem is intractable. There is no best training data or best algorithm for your problem, only the best that you can discover. The application of machine learning is best thought of as search problem for the best mapping of […]
