Lecture

Classification pipeline: building and evaluating

Related lectures (32)

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Evaluation in NLP

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

Image Classification: Decision Trees & Random Forests

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Classification: Decision Trees and kNN

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

Decision Tree Classification

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

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Hydroacoustics for Hydroelectric Installations

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

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Receiver-Operator Characteristics: ROC Curves

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

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Hydroacoustics for Hydroelectric Installations

Explores valve closing laws, water hammer, and surge tanks in hydroelectric installations.

Image Classification: Overfitting and Accuracy Measures

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

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.