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I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it takes a lot of time and sometimes it is obtained by a non-optimal method and exhibits a non-optimal performance.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

Notebook and database attached:

https://drive.google.com/file/d/1RV8B8KA1ls_PJvEe0JZcRL69KXdELhUq/view?usp=sharing

https://drive.google.com/file/d/1mmH_IwXfGjSrcdargg7rHPZe5Cd8Fc5D/view?usp=sharing

I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it takes a lot of time and sometimes it is obtained by a non-optimal method and exhibits a non-optimal performance.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

Notebook and database attached:

• notebook
• data

I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it takes a lot of time and sometimes it is obtained by a non-optimal method.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

Notebook and database attached:

https://drive.google.com/file/d/1RV8B8KA1ls_PJvEe0JZcRL69KXdELhUq/view?usp=sharing

https://drive.google.com/file/d/1mmH_IwXfGjSrcdargg7rHPZe5Cd8Fc5D/view?usp=sharing

I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it takes a lot of time and sometimes it is obtained by a non-optimal method and exhibits a non-optimal performance.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

Notebook and database attached:

https://drive.google.com/file/d/1RV8B8KA1ls_PJvEe0JZcRL69KXdELhUq/view?usp=sharing

https://drive.google.com/file/d/1mmH_IwXfGjSrcdargg7rHPZe5Cd8Fc5D/view?usp=sharing

I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it is always a classifier obtained by the LogisticRegresion method, and it is not optimal.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

I believe that I encountered a bug in Mathematica 12.

The Classify[] function throws errors when simultaneously:

1. The training set has significantly above $10^5$ examples.
2. Method->"_" option is absent, i.e. the procedure for searching for the optimal algorithm is active.

(When any of these conditions are changed, the classifier training proceeds correctly.)

The problem depends on the Mathematica version. (I will elaborate this idea.)

The following errors appear together (repeated several times):

a)

NetTrain :: encgenfail2: Could not encode one or more inputs for "Output" port: supplied data was a length-64 vector of real numbers, but expected a class. The invalid inputs had indices {158629, ..., << 14 >>}

b)

LibraryFunction :: typerr: An error occurred in the tree_evaluation.

c)

Part :: pkspec1: The expression -LibraryFunctionError [LIBRARY_TYPE_ERROR, 1] can not be used as a part specification.

and subsequent errors related to list and iteration indices and function domains.

The function in question returns a working classifier, but it takes a lot of time and sometimes it is obtained by a non-optimal method.

It seems that for large (but not very large!) data sets, the optimization procedure of the classification method fails.

Does anyone of you have an idea how to force it to work properly or is there any hope for a patch?

Performing the classification with specified methods and selecting the best is not a satisfactory solution, among others because each method has its own variants and they have some meta- parameters that are optimized. I do not know if in case of specified method, optimization within its variants and meta-parameters is done, or their default values ​​are used. On smaller data sets, where both approaches work, you can notice a worse performance of classification with a certain method, even if it is the one that the automatic search finds the best.

In version 11.3, this error did not show up. I do not know, however, whether it was absent or simply invisible, because it seemed to me that the performance of the classification was (with large sets) insufficient and the method choice surprising.

Notebook and database attached:

https://drive.google.com/file/d/1RV8B8KA1ls_PJvEe0JZcRL69KXdELhUq/view?usp=sharing

https://drive.google.com/file/d/1mmH_IwXfGjSrcdargg7rHPZe5Cd8Fc5D/view?usp=sharing