support vector machine SVM

Support Vector Machines (SVMs):

 

 These are the most recent supervised machine learning technique.

 Support Vector Machine (SVM) models are closely related to classical multi layer perceptron neural networks.

SVMs revolve around the notion of a margin either side of a hyperplane that separates two data classes.

 Maximizing the margin and thereby creating the largest possible distance between the separating hyperplane and the instances on either side of it has been proven to reduce an upper bound on the expected generalization error.

Decision Tree algorithm in python

Decision Trees:
 

Decision Trees (DT) are trees that classify instances by sorting them based on feature values.

Each node in a decision tree represents a feature in an instance to be classified, and each branch represents a value that the node can assume.

 Instances are classified starting at the root node and sorted based on their feature values.

Decision tree learning, used in data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.

 More descriptive names for such tree models are classification trees or regression trees.

Decision tree classifiers usually employ post-pruning techniques that evaluate the performance of decision trees, as they are pruned by using a validation set.

Any node can be removed and assigned the most common class of the training instances that are sorted to it.