There are 3 different approaches to evaluate the quality of predictions of a model: Estimator score

Linear Discriminant Analysis (

sklearn.neighbors

For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional

When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes? theorem with the ?naive? assumption of independence between every pair of features. Given

Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to discriminative learning of linear classifiers under convex loss functions

The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the input variables. In mathematical

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised