Various Classifiers for IRIS Dataset Classification

This collection demonstrates various classifiers for categorizing IRIS data through unsupervised classification of multi-dimensional sampling points. The implementations allow modification based on category count and represent partial code from pattern recognition assignments. Below are examples of classifiers implemented for IRIS dataset classification: 1. Decision Tree Classifier: Partitions data based on feature values through recursive binary splitting, constructing a tree structure where each node represents a feature threshold test. Implementation typically involves calculating Gini impurity or information gain to determine optimal splits, with scikit-learn's DecisionTreeClassifier providing built-in parameters for max_depth and min_samples_split optimization. 2. Support Vector Machine Classifier: Constructs hyperplanes in feature space using kernel functions (linear/RBF) to maximize margin separation between classes. The implementation utilizes Lagrange multipliers and quadratic programming, where scikit-learn's SVC class allows tuning of C (regularization) and gamma parameters for handling non-linear boundaries through kernel trick transformations. 3. Naive Bayes Classifier: Applies Bayes' theorem with strong feature independence assumptions, calculating posterior probabilities using Gaussian/Multinomial distributions. The algorithm estimates likelihood parameters through maximum likelihood estimation, with scikit-learn's GaussianNB implementing efficient probability calculations using logarithmic transformations to prevent underflow. 4. K-Nearest Neighbors Classifier: Determines class membership by majority voting among K closest training samples using distance metrics (Euclidean/Mahalanobis). The implementation involves constructing KD-trees for efficient nearest-neighbor searches, where scikit-learn's KNeighborsClassifier allows dynamic adjustment of K values and weight functions (uniform/distance-based). These classifiers represent fundamental algorithms for unsupervised classification of IRIS data. Selection can be based on specific requirements and category count, with flexibility to modify hyperparameters and underlying code structures. The implementations include critical optimization techniques such as cross-validation for parameter tuning and feature scaling for distance-based algorithms. These examples are designed to assist with classification tasks in pattern recognition assignments, providing practical code foundations for experimental comparisons and algorithmic enhancements.