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View Code? Open in Web Editor NEWEvaluation of Methods for dealing with Missing data in Machine Learning algorithms
Evaluation of Methods for dealing with Missing data in Machine Learning algorithms
Only two errors (internal as I remember well) occurred a few times.
On tasks: 3722, 29, 14954, 48:
Error in 1L:ncol(Y) : argument ma długość 0
and
INFO [18:15:01.269] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3561: profb (Supervised Classification)' (iter 1/5)
[1] "IRMI dont work on selcted params runing on defoult"
Error in VIM::irmi(df, imp_var = F) :
factor with less than 2 levels detected! -Overtime
INFO [14:57:48.084] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 48: heart-c (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'amelia.prep(x = x, m = m, idvars = idvars, empri = empri, ts = ts, ':
You have a small number of observations, relative to the number, of variables in the imputation model. Consider removing some variables, or reducing the order of time polynomials to reduce the number of parameters.error: inv_sympd(): matrix is singular or not positive definite
error: inv_sympd(): matrix is singular or not positive definite
error: inv_sympd(): matrix is singular or not positive definite
error: inv_sympd(): matrix is singular or not positive definite
The resulting variance matrix was not invertible. Please check your data for highly collinear variables.
INFO [22:14:35.457] Applying learner 'imput_VIM_regrImp.encodeimpact.classif.glmnet' on task 'Task 3543: irish (Supervised Classification)' (iter 4/5)
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])':
group ‘Senior_cycle_incomplete-secondary_school’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])':
group ‘Senior_cycle_incomplete-secondary_school’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])':
group ‘Senior_cycle_incomplete-secondary_school’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])':
group ‘Senior_cycle_incomplete-secondary_school’ is empty
character(0)
[1] "Error in doTryCatch(return(expr), name, parentenv, handler): \n"
Error in try({ :
Tasks in which probably left missings after imputation (the same situation as discussed here):
Test version without preprocessing of datasets.
Test log: mice log
INFO [09:28:14.248] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task '3807' (iter 1/5)
Ostrzeżenie: Number of logged events: 1
INFO [09:28:15.452] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task '3807' (iter 2/5)
Ostrzeżenie: Number of logged events: 25
INFO [09:28:17.239] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task '3807' (iter 3/5)
INFO [09:28:18.542] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task '3807' (iter 4/5)
Ostrzeżenie: Number of logged events: 40
INFO [09:28:19.834] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task '3807' (iter 5/5)of 5 iterations
- Task: 3807
- Learner: imput_mice.encodeimpact.classif.glmnet
- Warnings: 0 in 0 iterations
- Errors: 0 in 0 iterations
PROBABLY LEFT MISSINGS AFTER IMPUTATION!
INFO [22:12:39.235] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3830: cars (Supervised Classification)' (iter 3/5)
Error : Processed output task during prediction of imput_softImpute does not match output task during training.
INFO [15:07:05.868] Applying learner 'imput_missForest.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 2/5)
Ostrzeżenie w poleceniu 'randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, ':
The response has five or fewer unique values. Are you sure you want to do regression?
Ostrzeżenie w poleceniu 'randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, ':
The response has five or fewer unique values. Are you sure you want to do regression?
Ostrzeżenie w poleceniu 'randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, ':
The response has five or fewer unique values. Are you sure you want to do regression?
Ostrzeżenie w poleceniu 'randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, ':
The response has five or fewer unique values. Are you sure you want to do regression?
Ostrzeżenie w poleceniu 'randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, ':
The response has five or fewer unique values. Are you sure you want to do regression?
Error in[.data.frame
(final, , i) : nie wybrano kolumn
INFO [22:12:59.469] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 5/5)
of 5 iterations
- Task: Task 3722: hungarian (Supervised Classification)
- Learner: imput_VIM_HD.encodeimpact.classif.glmnet
- Warnings: 0 in 0 iterations
- Errors: 0 in 0 iterations
PROBABLY LEFT MISSINGS AFTER IMPUTATION!
INFO [14:57:59.713] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 3838: autos (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors, ':The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
Error in
contrasts<-
(*tmp*
, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
INFO [11:51:44.076] Applying learner 'imput_missMDA_MCA_PCA_FMAD.encodeimpact.classif.glmnet' on task 'Task 3561: profb (Supervised Classification)' (iter 1/5)
[1] "Fail to estimate ncp"
Error in if (any(MM[[g]] < 0)) stop(paste("The algorithm fails to converge. Choose a number of components (ncp) less or equal than ", :
brakuje wartości tam, gdzie wymagane jest TRUE/FALSE
INFO [14:59:09.899] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 29: credit-approval (Supervised Classification)' (iter 1/5)
...
Ostrzeżenie: glm.fit: fitted probabilities numerically 0 or 1 occurred
Ostrzeżenie w poleceniu 'multinom(form, data = x_reg, summ = 2, maxit = 50, trace = FALSE, ':
group ‘3’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data = x_reg, summ = 2, maxit = 50, trace = FALSE, ':
groups ‘2’ ‘3’ are empty
[1] "IRMI dont work on selcted params runing on defoult"
Ostrzeżenie w poleceniu 'multinom(form, data = x_reg, summ = 2, maxit = 50, trace = FALSE, ':
groups ‘2’ ‘3’ are empty
Error in 1L:ncol(Y) : argument ma długość 0
INFO [14:59:36.674] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3561: profb (Supervised Classification)' (iter 1/5)
[1] "IRMI dont work on selcted params runing on defoult"
Error in VIM::irmi(df, imp_var = F) :
factor with less than 2 levels detected! -Overtime
INFO [14:57:37.314] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Amelia Error Code: 4
The data has a column that is completely missing or only has one,observation. Remove these columns: ca
INFO [14:57:45.406] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 3830: cars (Supervised Classification)' (iter 1/5)
Amelia Error Code: 36
The number of categories in the nominal variable 'name' is greater than one-third of the observations.
INFO [22:15:13.400] Applying learner 'imput_missRanger.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 3/5)
Ostrzeżenie: Dropped unused factor level(s) in dependent variable: 3.
Error in pmm(xtrain = fit$predictions, xtest = pred, ytrain = data[[v]][!v.na], :
zmienna sum(ok <- !is.na(xtrain) & !is.na(ytrain)) >= 1L nie ma wartości TRUE
Error in impute(X, group = group, ncp = ncp, type = type, method = method, :
The algorithm fails to converge. Choose a number of components (ncp) less or equal than 0 or a number of iterations (maxiter) less or equal than 997
INFO [22:12:57.002] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
INFO [22:12:57.537] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 2/5)
INFO [22:12:58.449] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 3/5)
INFO [22:12:58.997] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 4/5)
INFO [22:12:59.469] Applying learner 'imput_VIM_HD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 5/5)of 5 iterations
- Task: Task 3722: hungarian (Supervised Classification)
- Learner: imput_VIM_HD.encodeimpact.classif.glmnet
- Warnings: 0 in 0 iterations
- Errors: 0 in 0 iterations
PROBABLY LEFT MISSINGS AFTER IMPUTATION!
INFO [22:12:48.043] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3675: pbc (Supervised Classification)' (iter 3/5)
Ostrzeżenie w poleceniu '[<-.factor
(*tmp*
, is.na(col_to_imp), value = "NA's")':
niepoprawny poziom czynnika, wygenerowano wartość NA
INFO [22:12:48.876] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3675: pbc (Supervised Classification)' (iter 4/5)
INFO [22:12:49.340] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3675: pbc (Supervised Classification)' (iter 5/5)of 5 iterations
- Task: Task 3675: pbc (Supervised Classification)
- Learner: imput_softImpute.encodeimpact.classif.glmnet
- Warnings: 0 in 0 iterations
- Errors: 0 in 0 iterations
PROBABLY LEFT MISSINGS AFTER IMPUTATION!
Test version without preprocessing of datasets.
Test log: IRMI
INFO [09:37:51.610] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task '3802' (iter 1/5)
[1] "IRMI dont work on selcted params runing on defoult"
Error in colnames(final) : nie znaleziono obiektu 'final'
INFO [22:32:23.834] Applying learner 'imput_missMDA_MCA_PCA_FMAD.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
[1] "Fail to estimate ncp"
Error in eigen(crossprod(X, X), symmetric = TRUE) :
wartość nieskończona lub brakuje wartości w 'x'
INFO [15:08:02.454] Applying learner 'imput_missForest.encodeimpact.classif.glmnet' on task 'Task 3561: profb (Supervised Classification)' (iter 1/5)
Error in[<-.data.frame
(*tmp*
, misi, res$varInd, value = structure(c(1L, :
zamiana ma 537 wierszy, dane mają 513
INFO [22:10:54.599] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Ostrzeżenie: Number of logged events: 51
Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, :
wartość NA/NaN/Inf w wywołaniu obcej funcji (argument 5)
INFO [22:11:41.559] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task 'Task 14954: cylinder-bands (Supervised Classification)' (iter 1/5)
Error in solve.default(xtx + diag(pen)) :
system jest obliczeniowo osobliwy: numer odwrotnego warunku = 1.96433e-16
INFO [14:57:46.758] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 14954: cylinder-bands (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors, ':The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
Ostrzeżenie w poleceniu 'amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors, ':
The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
Amelia Error Code: 43
You have a variable in your dataset that does not vary. Please remove this variable. Variables that do not vary: cylinder_division, ink_color
INFO [14:59:23.079] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3830: cars (Supervised Classification)' (iter 1/5)
...
Ostrzeżenie w poleceniu 'predict.lm(object, newdata, se.fit, scale = residual.scale, type = if (type == ':
prediction from a rank-deficient fit may be misleading
Error : Processed output task during prediction of imput_VIM_IRMI does not match output task during training.
INFO [15:05:12.329] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3838: autos (Supervised Classification)' (iter 1/5)
...
Ostrzeżenie: glm.fit: algorithm did not converge
Ostrzeżenie: glm.fit: fitted probabilities numerically 0 or 1 occurred
Error : Processed output task during prediction of imput_VIM_IRMI does not match output task during training.
INFO [22:12:39.235] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3830: cars (Supervised Classification)' (iter 3/5)
Error : Processed output task during prediction of imput_softImpute does not match output task during training.
Test version with preprocessing of datasets.
Test log: VIM_regrImp log
INFO [10:15:48.372] Applying learner 'imput_VIM_regrImp.encodeimpact.classif.glmnet' on task '3604' (iter 1/5)
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])': group ‘4’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])': group ‘4’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])': group ‘4’ is empty
Ostrzeżenie w poleceniu 'multinom(form, data[TFna, ])': group ‘4’ is empty
[1] "Error in apply(pre, 1, function(x) sample(1:length(x), 1, prob = x)): 'dim(X)' musi mieć dodatnią długość\n"
[1] "Error in apply(pre, 1, function(x) sample(1:length(x), 1, prob = x)): 'dim(X)' musi mieć dodatnią długość\n"
Error in apply(pre, 1, function(x) sample(1:length(x), 1, prob = x)) :
'dim(X)' musi mieć dodatnią długość
It is probably an internal problem already detected but with a changed error message. Because I am not sure - reporting it.
INFO [22:12:33.475] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu '[<-.factor
(*tmp*
, is.na(col_to_imp), value = "NA's")':
niepoprawny poziom czynnika, wygenerowano wartość NA
Ostrzeżenie w poleceniu '[<-.factor
(*tmp*
, is.na(col_to_imp), value = "NA's")':
niepoprawny poziom czynnika, wygenerowano wartość NA
Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, :
wartość NA/NaN/Inf w wywołaniu obcej funcji (argument 5)
Probably this error is thrown by glmnet when missing values were present in data.
INFO [15:05:10.585] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3847: analcatdata_draft (Supervised Classification)' (iter 1/5)
No missings in x. Nothing to impute
Ostrzeżenie w poleceniu 'kNN(x, imp_var = FALSE, mixed = mixed, mixed.constant = mixed.constant)':
Nothing to impute, because no NA are present (also after using makeNA)
Error : Processed output task during prediction of imput_VIM_IRMI does not match output task during training.
INFO [22:26:33.665] Applying learner 'imput_missMDA_MFA.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Error in eigen(crossprod(X, X), symmetric = TRUE) :
wartość nieskończona lub brakuje wartości w 'x'
INFO [22:12:33.475] Applying learner 'imput_softImpute.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu '[<-.factor
(*tmp*
, is.na(col_to_imp), value = "NA's")':
niepoprawny poziom czynnika, wygenerowano wartość NA
Ostrzeżenie w poleceniu '[<-.factor
(*tmp*
, is.na(col_to_imp), value = "NA's")':
niepoprawny poziom czynnika, wygenerowano wartość NA
Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, :
wartość NA/NaN/Inf w wywołaniu obcej funcji (argument 5)
INFO [15:05:12.329] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3838: autos (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'predict.lm(object, newdata, se.fit, scale = 1, type = if (type == ':
prediction from a rank-deficient fit may be misleading
...
Ostrzeżenie: glm.fit: fitted probabilities numerically 0 or 1 occurred
Ostrzeżenie: glm.fit: algorithm did not converge
...
Error : Processed output task during prediction of imput_VIM_IRMI does not match output task during training.
INFO [22:12:03.409] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task 'Task 3847: analcatdata_draft (Supervised Classification)' (iter 2/5)
Error in edit.setup(data, setup, ...) :
mice
detected constant and/or collinear variables. No predictors were left after their removal.
INFO [22:26:55.985] Applying learner 'imput_missMDA_MFA.encodeimpact.classif.glmnet' on task 'Task 3838: autos (Supervised Classification)' (iter 2/5)
Error in apply(tabdisj[, (vec[i] + 1):vec[i + 1]], 1, which.max) :
'dim(X)' musi mieć dodatnią długość
INFO [22:13:38.048] Applying learner 'imput_VIM_kNN.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 3/5)
[1] 5
[1] 5
Ostrzeżenie w poleceniu 'VIM::kNN(df, k = k, numFun = numFun, catFun = catFun, imp_var = F)':
All observations of feature are missing, therefore the variable will not be imputed!Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w min; zwracanie wartości Inf
Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w max; zwracanie wartości -Inf
Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w min; zwracanie wartości Inf
Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w max; zwracanie wartości -Inf
Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w min; zwracanie wartości Inf
Ostrzeżenie w poleceniu 'FUN(newX[, i], ...)':
brak argumentów w max; zwracanie wartości -Inf
Error in indexNA2s[, variable[j]] : indeks jest poza granicami
INFO [14:57:49.855] Applying learner 'imput_Amelia.encodeimpact.classif.glmnet' on task 'Task 3561: profb (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors, ':The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
Ostrzeżenie w poleceniu 'amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors, ':
The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
Amelia Error Code: 43
You have a variable in your dataset that does not vary. Please remove this variable. Variables that do not vary: Overtime
INFO [15:07:47.108] Applying learner 'imput_missForest.encodeimpact.classif.glmnet' on task 'Task 3830: cars (Supervised Classification)' (iter 1/5)
Error in { :
task 1 failed - "Can not handle categorical predictors with more than 53 categories."INFO [15:07:48.802] Applying learner 'imput_missForest.encodeimpact.classif.glmnet' on task 'Task 14954: cylinder-bands (Supervised Classification)' (iter 1/5)
Error in { :
task 1 failed - "Can not handle categorical predictors with more than 53 categories."
Adding separate PipeOpPreproces function is required to create columns with the information where imputation will happen. I will do this. This issue is only to inform you about the problem with the current solution.
INFO [15:05:10.585] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3847: analcatdata_draft (Supervised Classification)' (iter 1/5)
No missings in x. Nothing to impute
Ostrzeżenie w poleceniu 'kNN(x, imp_var = FALSE, mixed = mixed, mixed.constant = mixed.constant)':
Nothing to impute, because no NA are present (also after using makeNA)
Error : Processed output task during prediction of imput_VIM_IRMI does not match output task during training.
INFO [14:58:23.920] Applying learner 'imput_VIM_IRMI.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Ostrzeżenie w poleceniu 'multinom(form, data = x_reg, summ = 2, maxit = 50, trace = FALSE, ':
group ‘0’ is empty
[1] "IRMI dont work on selcted params runing on defoult"
Ostrzeżenie w poleceniu 'predict.lm(object, newdata, se.fit, scale = 1, type = if (type == ':
prediction from a rank-deficient fit may be misleading
Ostrzeżenie: glm.fit: algorithm did not converge
Ostrzeżenie: glm.fit: fitted probabilities numerically 0 or 1 occurred
Ostrzeżenie w poleceniu 'predict.lm(object, newdata, se.fit, scale = 1, type = if (type == ':
prediction from a rank-deficient fit may be misleading
Ostrzeżenie w poleceniu 'multinom(form, data = x_reg, summ = 2, maxit = 50, trace = FALSE, ':
group ‘0’ is empty
Error in 1L:ncol(Y) : argument ma długość 0
test R script: script
PipeOpTaskPreproc version results:
Amelia (PipeOpTaskPreproc)
VIM_IRMI (PipeOpTaskPreproc)
missForest (PipeOpTaskPreproc)
mice (PipeOpTaskPreproc)
softImpute (PipeOpTaskPreproc)
VIM_HD (PipeOpTaskPreproc)
VIM_KNN (PipeOpTaskPreproc)
VIM_regrImp (PipeOpTaskPreproc)
missRanger (PipeOpTaskPreproc)
missMDA_MFA (PipeOpTaskPreproc)
missMDA_MCA_PCA_FMAD (PipeOpTaskPreproc)
INFO [22:10:54.599] Applying learner 'imput_mice.encodeimpact.classif.glmnet' on task 'Task 3722: hungarian (Supervised Classification)' (iter 1/5)
Ostrzeżenie: Number of logged events: 51
Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, :
wartość NA/NaN/Inf w wywołaniu obcej funcji (argument 5)
update 23.07.2020:
INFO [22:26:44.621] Applying learner 'imput_missMDA_MFA.encodeimpact.classif.glmnet' on task 'Task 14954: cylinder-bands (Supervised Classification)' (iter 1/5)
Error in if (any(MM[[g]] < 0)) stop(paste("The algorithm fails to converge. Choose a number of components (ncp) less or equal than ", :
brakuje wartości tam, gdzie wymagane jest TRUE/FALSE
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