Category: Machine Learning

Tweet Share Share Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is…

Tweet Share Share Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike…

Tweet Share Share Gradient boosting is an ensemble of decision trees algorithms. It may be one of the most popular techniques for structured (tabular) classification…

Tweet Share Share Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset. This is…

  Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It…

Tweet Share Share Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles. Taken at…

Tweet Share Share Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine…

Tweet Share Share Last Updated on December 23, 2020 Knowledge of calculus is not required to get results and solve problems in machine learning or…

Tweet Share Share Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling. The technique involves fitting multiple machine learning models…

Tweet Share Share The Perceptron is a linear machine learning algorithm for binary classification tasks. It may be considered one of the first and one…