Model Evaluation: K-Nearest Neighbor

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (37)

Classification with GMM and kNN Log in to Mediaspace to watch this video

Covers classification using GMM and kNN, exploring boundaries, errors, and practical exercises.

Image Classification: Overfitting and Accuracy Measures Log in to Mediaspace to watch this video

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.

Logistic Regression: Probabilistic Interpretation Log in to Mediaspace to watch this video

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Model Evaluation Log in to Mediaspace to watch this video

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Machine Learning Fundamentals: Overfitting and Regularization Log in to Mediaspace to watch this video

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

Receiver-Operator Characteristics: ROC Curves Log in to Mediaspace to watch this video

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Polynomial Regression: Basics and Regularization Log in to Mediaspace to watch this video

Covers the basics of polynomial regression and regularization to prevent overfitting.

Cross-validation & Regularization Log in to Mediaspace to watch this video

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Data Representations & Processing Log in to Mediaspace to watch this video

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Machine Learning Fundamentals: Regularization and Cross-validation Log in to Mediaspace to watch this video

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Classification pipeline: building and evaluating Log in to Mediaspace to watch this video

Explains building and evaluating a classification pipeline using tweet data sets.

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Hydroacoustics for Hydroelectric Installations Log in to Mediaspace to watch this video

Explores hydroacoustic simulation parameters, mass oscillation modeling, and validation errors in hydroelectric installations.

Machine Learning Basics Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Optimization in Machine Learning Log in to Mediaspace to watch this video

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Discrete Fourier Transform: Introduction and Sampling Log in to Mediaspace to watch this video

Covers the introduction of discrete Fourier transform and its implications on signal reconstruction.

Page 2 of 2