Concept
Decision tree pruning
Related lectures (15)
Ground Penetrating Radar: Data Analysis
Explores the automated picking of reinforcement bars within Ground Penetrating Radar data using machine learning and signal processing techniques.
Decision Trees: Classification
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Structured Classifications: Decision Trees and Boosting
Explores decision trees, overfitting elimination, boosting techniques, and their practical applications in predictive modeling.
Decision Trees and Boosting
Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Supervised Learning: k-NN and Decision Trees
Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.
Deliberative Agents: Planning and Strategies
Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.
Introduction to Image Classification
Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Kinetic Isotope Effects and Linear Free Energy Relationships
Explores kinetic isotope effects and Linear Free Energy Relationships, introducing machine learning methods for chemistry applications.
Feature Extraction & Clustering Methods
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.