Rapid Fire on Machine Learning

In order to shatter a given configuration of points, a classifier must be able to, for all possible assignments of positive and negative for the points, perfectly partition the plane such that positive points are separated from negative points. For a configuration of n points, there are 2n possible assignments of positive or negative. When … Read more

A parameter is a variable that is internal to the model and whose value is estimated from the training data. They are often saved as part of the learned model. Examples include weights, biases etc. A hyperparameter is a variable that is external to the model whose value cannot be estimated from the data. They … Read more

A generative model learns the different categories of data. On the other hand, a discriminative model will only learn the distinctions between different categories of data. Discriminative models perform much better than the generative models when it comes to classification tasks. Generative and discriminative models are two fundamental approaches in machine learning that serve different … Read more

The gamma defines influence. Low values meaning ‘far’ and high values meaning ‘close’. If gamma is too large, the radius of the area of influence of the support vectors only includes the support vector itself and no amount of regularization with C will be able to prevent overfitting. If gamma is very small, the model … Read more

Bagging and Boosting are variants of Ensemble Techniques. Bootstrap Aggregation or bagging is a method that is used to reduce the variance for algorithms having very high variance. Decision trees are a particular family of classifiers which are susceptible to having high bias. Decision trees have a lot of sensitiveness to the type of data … Read more