evaluate_imputation: Imputation Evaluation
In yatzy/xgbimpute: Imputation Using XGBoost
Description
Usage
Arguments
Value
Examples
Description
This function calculates for evaluation metrics between matrix with no missigness and matrix with imputed values. Four metrics will be returned: root mean square error, mean absolute error, coeficient of variation of root mean square error and mean absolute percentage error for each column in the matrices.
Usage
1
evaluate_imputation(original_matrix, imputed_matrix)
Arguments
original_matrix
Original matrix
imputed_matrix
Matrix for which missigness has been for example artificially created and then imputed
Value
matrix containing rmse, mae, rmse_cv and mape for each column
Examples
1
2
3
4
mat = as.matrix(iris[,1:4])
mis_mat = generate_na(mat , 0.3)
imp_mat = impute_xgboost(mis_mat)
evaluate_imputation(mat, imp_mat)
yatzy/xgbimpute documentation built on June 7, 2019, 8:16 p.m.
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Description Usage Arguments Value Examples
Description
This function calculates for evaluation metrics between matrix with no missigness and matrix with imputed values. Four metrics will be returned: root mean square error, mean absolute error, coeficient of variation of root mean square error and mean absolute percentage error for each column in the matrices.
Usage
1 | evaluate_imputation(original_matrix, imputed_matrix)
|
Arguments
original_matrix |
Original matrix |
imputed_matrix |
Matrix for which missigness has been for example artificially created and then imputed |
Value
matrix containing rmse, mae, rmse_cv and mape for each column
Examples
1 2 3 4 | mat = as.matrix(iris[,1:4])
mis_mat = generate_na(mat , 0.3)
imp_mat = impute_xgboost(mis_mat)
evaluate_imputation(mat, imp_mat)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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