Statistical model validation

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Data analysis
Cross-validation

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Related lectures (32)

Using cross-validation: Building a final predictor

Covers the interpretation of cross-validation risk estimates and building a final predictor from cross-validation results.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Regression Trees and Ensemble Methods in Machine Learning

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

Prototyping with SP80 - MAKE project

Highlights the significance of prototyping through the SP80 project.

Polynomial Regression: Basics and Regularization

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

Regularization Methods: Training and Validation Base

Explores regularization methods in neural networks, emphasizing the importance of training and validation bases to prevent overfitting.

Playing Connect Four

Explores the implementation of Connect Four game functionalities and testing methods.

Logistic Regression: Probabilistic Interpretation

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

Machine Learning Basics

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

Cross-validation & Regularization

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

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