Category solved model papers

Fundamentals of Bayes’ Theorem Bayes’ theorem uses three types of probabilities: Bayes Theorem: Classification Using Bayes Model

A regression tree is a type of decision tree used when the target attribute is continuous (not categorical). Instead of classifying into categories, the goal is to predict a real…

C4.5 is an improved version of ID3. It addresses the limitations of ID3 and enhances its capabilities. Features of C4.5: Algorithm : C4.5 Decision Tree Construction Input: Training dataset TOutput:…

ID3 (Iterative Dichotomiser 3) is a supervised learning algorithm that constructs a decision tree using a greedy approach. It selects the best attribute at each level of the tree using…

A Decision Tree is a supervised learning model used for both classification and regression tasks. It classifies data instances with high accuracy and interpretability. The model follows an inductive inference…

Instance-based learning is called lazy learning because: Examples: Comparison: Instance-based vs. Model-based Learning Aspect Instance-Based Learning (Lazy) Model-Based Learning (Eager) Model creation No explicit model is created Learns a model…

Types of Regression Methods Limitations of Regression Methods

The Nearest Centroid Classifier is a simple alternative to k-Nearest Neighbors (k-NN) for similarity-based classification. It is also known as the Mean Difference Classifier. The key idea of this classifier…

The Weighted k-NN algorithm is an extension of the standard k-NN algorithm. Instead of treating all k neighbors equally, it assigns weights to neighbors based on their distance from the…

The k-NN algorithm relies on the assumption that similar objects exist close to each other in the feature space. It performs instance-based learning, meaning it stores the training instances and…