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ML.pptx
- 1. NADAR SARASWATHI COLLEGEOF ARTS AND SCIENCE Classification tree and gradient descent algorithm P.Gayathri II M.Sc ComputerScience ARTIFICIALINTELLIGENCEANDMACHINELEARNING
- 2. Decision Tree isa Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.
- 3. In a Decisiontree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. The decisions or the test are performed on the basis of features of the given dataset.
- 4. It is agraphical representation for getting all the possible solutions to a problem/decision based on given conditions. It is called a decision tree because, similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure.
- 5. In order tobuild a tree, we use the CART algorithm, which stands for Classification and Regression Tree algorithm. A decision tree simply asks a question, and based on the answer (Yes/No), it further split the tree into subtrees. Below diagram explains the general structure of a decision tree:
- 7. Decision Tree Terminologies •RootNode: Root node is from where the decision tree starts. It represents the entire dataset, which further gets divided into two or more homogeneous sets. •Leaf Node: Leaf nodes are the final output node, and the tree cannot be segregated further after getting a leaf node. •Splitting: Splitting is the process of dividing the decision node/root node into sub-nodes according to the given conditions. •Branch/Sub Tree: A tree formed by splitting the tree. •Pruning: Pruning is the process of removing the unwanted branches from the tree. •Parent/Child node: The root node of the tree is called the parent node, and other nodes are called the child nodes.
- 8. Gradient descent (GD)is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML) and deep learning (DL) to minimise a cost/loss function (e.g. in a linear regression).
- 9. However, its useis not limited to ML/DL only, it’s widely used also in areas like: control engineering (robotics, chemical, etc.) computer games mechanical engineering
- 10. Gradient descent algorithmdoes not work for all functions. There are two specific requirements. A function has to be: differentiable convex
- 11. Gradient Before jumping intocode one more thing has to be explained — what is a gradient. Intuitively it is a slope of a curve at a given point in a specified direction. In the case of a univariate function, it is simply the first derivative at a selected point. In the case of a multivariate function, it is a vector of derivatives in each main direction (along variable axes). Because we are interested only in a slope along one axis and we don’t care about others these derivatives are called partial derivatives.
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