User Andrew Cassidy

5 votes

Accepted

How are the clustering algorithms using the concept of Latent Semantic Analysis?

answered Mar 18, 2015 at 14:01

2 votes

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Problem using MLP function from package RSNNS in R

answered Jan 26, 2016 at 20:52

1 vote

Machine learning for multi-level response

answered Mar 15, 2014 at 21:50

1 vote

Accepted

unbalanced samples random Forests

answered Mar 17, 2014 at 22:19

1 vote

Comparing interaction effects using different variables. Possible?

answered Mar 25, 2014 at 14:03

1 vote

Benchmark Data-sets for Concept Drift where important predictors (independent variables) change with time or stream of observations

answered Oct 30, 2014 at 19:41

1 vote

Cross-validation and feature selection of a multivariate regression

answered Feb 24, 2014 at 17:12

1 vote

Using standardized regression coefficients to measure relative contribution

answered Feb 26, 2014 at 14:46

1 vote

Selecting the number of sparse principal components to include in regression

answered Mar 5, 2014 at 19:13

1 vote

R as a backend processing engine

answered Mar 7, 2014 at 16:56

0 votes

Accepted

Optimizing False Negative Rate after Logistic Regression

answered Mar 7, 2014 at 17:29

0 votes

Performing PCA with only a distance matrix

answered Feb 24, 2014 at 17:21

0 votes

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Random Forest Regression Overfitting - Quantile Test on Test Data

answered Feb 24, 2014 at 20:18

0 votes

How can a t-test be statistically significant if the mean difference is almost 0?

answered Feb 25, 2014 at 22:00

0 votes

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Trend Analysis of feature importance over time in R

answered Mar 17, 2014 at 21:32

0 votes

Accepted

Project new data using independent components analysis

answered Mar 26, 2016 at 16:00

-1 votes

Predict function and categorical variables in R

answered Mar 7, 2014 at 16:49

Stack Exchange Network

17 Answers

How are the clustering algorithms using the concept of Latent Semantic Analysis?

Problem using MLP function from package RSNNS in R

Machine learning for multi-level response

unbalanced samples random Forests

Comparing interaction effects using different variables. Possible?

Benchmark Data-sets for Concept Drift where important predictors (independent variables) change with time or stream of observations

Cross-validation and feature selection of a multivariate regression

Using standardized regression coefficients to measure relative contribution

Selecting the number of sparse principal components to include in regression

R as a backend processing engine

Optimizing False Negative Rate after Logistic Regression

Performing PCA with only a distance matrix

Random Forest Regression Overfitting - Quantile Test on Test Data

How can a t-test be statistically significant if the mean difference is almost 0?

Trend Analysis of feature importance over time in R

Project new data using independent components analysis

Predict function and categorical variables in R