After training and selection the SVM provides means to evaluate labels
for new input features.
If you do not have labels consider using predict.liquidSVM.
The errors for all tasks and cells are returned attached to the result (see errors).
# S3 method for liquidSVM
test(model, newdata, labels = NULL,
command.args = NULL, ..., d = NULL)the SVM model as returned by init.liquidSVM
data frame of features to predict.
If it has all the explanatory variables of formula, then the respective subset is taken.
NAs will be removed.
the known labels to test against. If NULL then they are retrieved from newdata using the original formula.
further arguments aranged in a list, corresponding to the arguments
of the command line interface to svm-select, e.g. list(d=2,R=0)
is equivalent to svm-select -d 2 -R 0.
See command-args for details.
other configuration parameters passed to testing phase
level of display information
predictions for all tasks together with errors (see errors).
This is also recorded in model$last_result.
The following parameters can be used as well:
GPU=c(<use_gpus>,[<GPU_offset>])
Flag controlling whether the GPU support is used. If <use_gpus> = 1, then each CPU thread gets a thread on a GPU. In the case of multiple GPUs, these threads are uniformly distributed among the available GPUs. The optional <GPU_offset> is added to the CPU thread number before the GPU is added before distributing the threads to the GPUs. This makes it possible to avoid that two or more independent processes use the same GPU, if more than one GPU is available.
Allowed values: <use_gpus>: bool <use_offset>: non-negative integer.
Default values: <gpus> = 0 <gpu_offset> = 0
Unfortunately, this option is not activated for the binaries you are currently using. Install CUDA and recompile to activate this option.
h=[<level>]
Displays all help messages.
Meaning of specific values: <level> = 0 => short help messages <level> = 1 => detailed help messages
Allowed values: <level>: 0 or 1
Default values: <level> = 0
L=c(<loss>,[<neg_weight>,<pos_weight>])
Sets the loss that is used to compute empirical errors. The optional weights can only be set, if <loss> specifies a loss that has weights.
Meaning of specific values: <loss> = 0 => binary classification loss <loss> = 1 => multiclass class <loss> = 2 => least squares loss <loss> = 3 => weighted least squares loss <loss> = 6 => your own template loss
Allowed values: <loss>: integer between 0 and 2 <neg_weight>: float > 0.0 <pos_weight>: float > 0.0
Default values: <loss> = 0 <neg_weight> = 1.0 <pos_weight> = 1.0
T=c(<threads>,[<thread_id_offset>])
Sets the number of threads that are going to be used. Each thread is assigned to a logical processor on the system, so that the number of allowed threads is bounded by the number of logical processors. On systems with activated hyperthreading each physical core runs one thread, if <threads> does not exceed the number of physical cores. Since hyper- threads on the same core share resources, using more threads than cores does usually not increase the performance significantly, and may even decrease it. The optional <thread_id_offset> is added before distributing the threads to the cores. This makes it possible to avoid that two or more independent processes use the same physical cores. Example: To run 2 processes with 3 threads each on a 6-core system call the first process with -T 3 0 and the second one with -T 3 3 .
Meaning of specific values: <threads> = 0 => 4 threads are used (all physical cores run one thread) <threads> = -1 => 3 threads are used (all but one of the physical cores run one thread)
Allowed values: <threads>: integer between -1 and 4 <thread_id_offset>: integer between 0 and 4
Default values: <threads> = 0 <thread_id_offset> = 0
v=c(<weighted>,<scenario>,[<npl_class>])
Sets the weighted vote method to combine decision functions from different folds. If <weighted> = 1, then weights are computed with the help of the validation error, otherwise, equal weights are used. In the classification scenario, the decision function values are first transformed to -1 and +1, before a weighted vote is performed, in the regression scenario, the bare function values are used in the vote. In the weighted NPL scenario, the weights are computed according to the validation error on the samples with label <npl_class>, the rest is like in the classification scenario. <npl_class> can only be set for the NPL scenario.
Meaning of specific values: <scenario> = 0 => classification <scenario> = 1 => regression <scenario> = 2 => NPL
Allowed values: <weighted>: 0 or 1 <scenario>: integer between 0 and 2 <npl_class>: -1 or 1
Default values: <weighted> = 1 <scenario> = 0 <npl_class> = 1
o=<display_roc_style>
Sets a flag that decides, wheather classification errors are displayed by true positive and false positives.
Allowed values: <display_roc_style>: 0 or 1
Default values: <display_roc_style>: Depends on option -v
If the SVM has multiple tasks the result will have corresponding columns.
For mcSVM the first column gives the global vote
and the other columns give the result for the corresponding binary classification problem
indicated by the column name.
For convenience the latest result is always saved in model$last_result.
# NOT RUN {
modelTrees <- svm(Height ~ Girth + Volume, trees[1:10, ]) # least squares
result <- test(modelTrees, trees[11:31, ], trees$Height[11:31])
errors(result)
# }
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