courseraАнализ данных

Problem-Dependent Resampling Techniques

Курс от 28DIGITAL

Средний≈ 5.8 чАнглийский

О курсе

This course is designed for data scientists, machine learning practitioners, and researchers who want to understand how resampling techniques must be adapted to the structure of the problem at hand. You will learn how standard validation methods such as cross-validation can fail when applied blindly, and how to design problem-dependent resampling strategies for spatial data, pair-input data, and other dependent observation structures. The course also covers spatial cross-validation, dependency-aware evaluation design, and statistical testing methods to assess whether performance estimates are reliable. By the end of the course, you will be able to choose and construct appropriate resampling strategies that reflect the true structure of your data and provide trustworthy performance estimates.

Навыки, которые вы освоите

Model Evaluation Spatial Data Analysis Statistical Hypothesis Testing Statistical Methods Analytical Skills Machine Learning Algorithms Analytics Data Synthesis Sample Size Determination Applied Machine Learning Feature Engineering Dependency Analysis Data Processing Drug Interaction Sampling (Statistics)Correlation Analysis Analysis Machine Learning Spatial Analysis

Программа курса

3 модулей · 24 учебных материалов

01Evaluating spatial models with spatial cross-validation10 материалов

Spatial cross-validation in forestry application

Учитесь у экспертов

Jonne Pohjankukka

Dr.

Asja Kamenica

Head of EIT Digital Professional School

Problem-Dependent Resampling Techniques

О курсе

Навыки, которые вы освоите

Программа курса

Introduction to spatial data and spatial autocorrelation

Spatial data and cross-validation

Spatial cross-validation

Spatial cross-validation (pseudocode)

Spatial cross-validation in forestry application

Учитесь у экспертов

Jonne Pohjankukka

Asja Kamenica

Sampling grid optimization via spatial cross-validation

Assignment and Discussion

Pair-input data

Prediction of drug-target interactions

Dependencies in pair-input data

Cross-validation for pair-input data

Selection of training examples

Assignment and Discussion

Introduction to statistical testing

Wilcoxon test for classifier evaluation

"Learning" from non-signal data

Feature selection bias

Permutation testing

Assignment and Discussion