Ensemble Methods: Random Forest

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Addressing Overfitting in Decision Trees

Explores overfitting in decision trees and introduces random forests as a solution.

Recurrent Neural Networks: Language Detection

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Decision Forests: Structure and Training

Covers decision forests, training, weak learners, entropy, boosting, 3D pose estimation, and practical applications.

Kinetic Isotope Effects and Linear Free Energy Relationships

Explores kinetic isotope effects and Linear Free Energy Relationships, introducing machine learning methods for chemistry applications.

Decision Trees: Overfitting and Randomization

Explores decision trees, overfitting, and randomization in supervised learning, emphasizing the importance of managing variance and feature selection.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.