Level 2 AIML Report
28 / 9 / 2024
Task 1: Decision Tree based ID3 Algorithm
The ID3 algorithm is a widely used decision tree classifier that employs a top-down, greedy approach to construct a decision tree by selecting attributes based on the concept of information gain. This method is effective for classification tasks, allowing for clear interpretability of decision-making processes. ID3 algorithm
Task 2: Naive Bayesian Classifier
The Naive Bayesian Classifier is a probabilistic model based on Bayes' theorem, which assumes that the features are conditionally independent given the class label. This classifier is particularly useful for text classification tasks, such as spam detection, due to its simplicity and efficiency. Naive Bayes
Task 3: Ensemble Techniques
Ensemble techniques combine multiple machine learning models to improve prediction accuracy and robustness. By leveraging the strengths of various algorithms, such as bagging and boosting, ensemble methods help reduce overfitting and enhance model performance across diverse datasets. Ensemble Technique
Task 4: Random Forest, GBM, and XGBoost
This task explores three powerful ensemble methods: Random Forest, Gradient Boosting Machines (GBM), and XGBoost. Each method utilizes different approaches to combine the predictions of several base learners, offering advantages in terms of accuracy and computational efficiency for classification and regression problems.
Task 5: Hyperparameter Tuning
Hyperparameter tuning is a crucial process in machine learning that involves optimizing the parameters that govern the training process of models. Techniques like Grid Search and Random Search are employed to identify the best hyperparameter settings, which can significantly influence the model's performance.Hyperparameter tuning
Task 6: Image Classification using KMeans Clustering
KMeans clustering is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. In the context of image classification, KMeans can be applied to group similar images together, facilitating tasks such as pattern recognition and organization of visual data.K means clustering and image classification
Task 7: Anomaly Detection
Anomaly detection involves identifying data points that deviate significantly from expected patterns, often indicating unusual behavior or errors in the dataset. This task encompasses various techniques to detect outliers, making it a vital component in applications such as fraud detection and monitoring system health.Anomaly detection