Boosted Trees

Creates a binary classifier using a boosted decision tree algorithm. Category: Machine Learning / Initialize Model / Classification. Module overview. This article describes how to use the Two-Class Boosted Decision Tree module in Azure Machine Learning Studio, to create a machine learning model that is based on the boosted decision trees algorithm.

source:
docs.microsoft.com

image:
breaking-bi.blogspot.co.uk

Decision Trees

A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature. The arcs coming from a node labeled with an input feature are labeled with each of the possible values of the target or output feature or the arc leads to a subordinate decision node on a different input feature.

source:
en.wikipedia.org

image:
databricks.com

Linear Classifiers: Logistic Regression, Naive Bayes Classifier

Both naive bayes and logistic regression are log-linear models; that is, in both cases the probability of a document belonging to a class is proportional to exp(w·x), where w is a classifier parameter and x is a feature vector for the document.

source:
quora.com

image:
slideplayer.com

Nearest Neighbor

Condensed nearest neighbor (CNN, the Hart algorithm) is an algorithm designed to reduce the data set for k-NN classification. It selects the set of prototypes U from the training data, such that 1NN with U can classify the examples almost as accurately as 1NN does with the whole data set.

source:
en.wikipedia.org

image:
slideserve.com

Neural Networks

Artificial neural networks are relatively crude electronic networks of neurons based on the neural structure of the brain. They process records one at a time, and learn by comparing their classification of the record (i.e., largely arbitrary) with the known actual classification of the record.

source:
solver.com

image:
jamesmccaffrey.wordpress.com

Random Forest

The first algorithm for random decision forests was created by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.

source:
en.wikipedia.org

image:
youtube.com

Support Vector Machines

The support vector clustering algorithm created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications.

source:
en.wikipedia.org

image:
www-personal.umich.edu