Все публикации

0:27:35

16 K-Nearest Neighbors Models in Supervised Learning

0:41:25

15 Supervised Learning with Gradient Boosting

0:30:40

14 Random Forest Models in Supervised Learning

0:39:19

Using a large language model to classify topics

0:32:11

16 Histogram-based Gradient Boosting Regression Tree

0:35:40

06 Model Complexity and Generalization in Supervised Learning

0:30:35

02 Preparing data for classification supervised learning

0:27:52

Geostatistics for Compositional Data with R - 4.4 Minimum/Maximum Autocorrelation factors (MAF)

0:18:38

Geostatistics for Compositional Data with R - 2.2 Log-Ratio Transformations

0:16:01

20 Generalized Estimating Equations Using geepack

0:19:56

16 Firth’s Bias-Reduced Logistic Regression

0:34:19

12 Multicollinearity in Logistic Regression Models

0:17:24

28 Blocking in Experimental Design

0:20:36

27 Resampling methods

0:25:19

26 Robust Regression Techniques

0:21:21

25 Goodness-of-fit Measures

0:18:52

24 Non-parametric statistics - rank-based tests

0:26:23

22 Generalized linear models

0:28:31

23 Non-linear regression

0:41:49

21 Model selection and validation

0:49:26

20 Multiple linear regression

0:42:34

19 Simple Linear Regression

0:27:41

18 ANOVA assumptions and diagnostics

0:31:18

17 Nested ANOVA