Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles. Taken at face value, the razor is a heuristic tha... Read more

Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to... Read more

Knowledge of calculus is not required to get results and solve problems in machine learning or deep learning. However, knowing some calculus will help you in a number of ways, such as in rea... Read more

Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling. The technique involves fitting multiple machine learning models on the training... Read more

The Perceptron is a linear machine learning algorithm for binary classification tasks. It may be considered one of the first and one of the simplest types of artificial neural networks. It i... Read more

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of encoder and a decoder sub-models. The encoder compres... Read more

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of an encoder and a decoder sub-models. The encoder comp... Read more

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 backpropaga... Read more

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... Read more

Blending is an ensemble machine learning algorithm. It is a colloquial name for stacked generalization or stacking ensemble where instead of fitting the meta-model on out-of-fold predictions... Read more