AIML Level-2 Report
30 / 12 / 2024
Lekha DH
5th Sem ISE
Task 1 - Decision Tree based ID3 Algorithm
A decision tree, which has a hierarchical structure made up of root, branches, internal, and leaf nodes, is a non-parametric supervised learning approach used for classification and regression applications.
Decision Tree Terminologies:
1.Root Node
2.Decision Node
3.Leaf Node
4.Sub-tree
5.Pruning
6.Parent and Child Node
ID3 Algorithm:
ID3 stands for Iterative Dichotomiser 3 and is named such because the algorithm iteratively (repeatedly) dichotomizes(divides) features into two or more groups at each step. ID3 Algorithm
Task 2 - Naive Bayesian Classifier
A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. The crux of the classifier is based on the Bayes theorem.
Bayes Theorem:
Task 3 - Ensemble techniques
Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models.
The three main classes of ensemble learning methods are bagging, stacking, and boosting, and it is important to both have a detailed understanding of each method and to consider them on your predictive modeling project.
Ensemble techniques on the Titanic Dataset:
Task 4-Anomaly Detection
Anomaly Detection is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations.
