public abstract static class Model.Parameters extends Iced
The non-transient fields are input parameters to the model-building process, and are considered "first class citizens" by the front-end - the front-end will cache Parameters (in the browser, in JavaScript, on disk) and rebuild Parameter instances from those caches. WARNING: Model Parameters is not immutable object and ModelBuilder can modify them!
Modifier and Type | Class and Description |
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static class |
Model.Parameters.FoldAssignmentScheme |
Modifier and Type | Field and Description |
---|---|
boolean |
_balance_classes
Should all classes be over/under-sampled to balance the class
distribution?
|
Key<? extends Model> |
_checkpoint
A model key associated with a previously trained Deep Learning
model.
|
float[] |
_class_sampling_factors
Desired over/under-sampling ratios per class (lexicographic order).
|
Distribution.Family |
_distribution |
Model.Parameters.FoldAssignmentScheme |
_fold_assignment |
java.lang.String |
_fold_column |
boolean |
_ignore_const_cols |
java.lang.String[] |
_ignored_columns |
boolean |
_keep_cross_validation_predictions |
float |
_max_after_balance_size
When classes are being balanced, limit the resulting dataset size to
the specified multiple of the original dataset size.
|
int |
_max_confusion_matrix_size
For classification models, the maximum size (in terms of classes) of
the confusion matrix for it to be printed.
|
int |
_max_hit_ratio_k
The maximum number (top K) of predictions to use for hit ratio
computation (for multi-class only, 0 to disable)
|
Key<Model> |
_model_id |
int |
_nfolds |
java.lang.String |
_offset_column |
java.lang.String |
_response_column
Supervised models have an expected response they get to train with!
|
boolean |
_score_each_iteration |
ScoreKeeper.StoppingMetric |
_stopping_metric
Metric to use for convergence checking, only for _stopping_rounds > 0
|
int |
_stopping_rounds
Early stopping based on convergence of stopping_metric.
|
double |
_stopping_tolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
|
Key<Frame> |
_train |
double |
_tweedie_power |
Key<Frame> |
_valid |
java.lang.String |
_weights_column |
static int |
MAX_SUPPORTED_LEVELS
Maximal number of supported levels in response.
|
Constructor and Description |
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Model.Parameters() |
Modifier and Type | Method and Description |
---|---|
protected long |
checksum_impl()
Compute a checksum based on all non-transient non-static ice-able assignable fields (incl.
|
long |
checksum() |
protected boolean |
defaultDropConsCols() |
protected boolean |
defaultDropNA20Cols() |
protected double |
defaultStoppingTolerance() |
boolean |
hasCheckpoint() |
double |
missingColumnsType()
Type of missing columns during adaptation between train/test datasets
Overload this method for models that have sparse data handling - a zero
will preserve the sparseness.
|
void |
read_lock_frames(Job job)
Read-Lock both training and validation User frames.
|
void |
read_unlock_frames(Job job)
Read-UnLock both training and validation User frames.
|
Frame |
train() |
Frame |
valid() |
clone, frozenType, read_impl, read, readExternal, readJSON_impl, readJSON, toJsonString, write_impl, write, writeExternal, writeJSON_impl, writeJSON
public static final int MAX_SUPPORTED_LEVELS
public int _nfolds
public boolean _keep_cross_validation_predictions
public Model.Parameters.FoldAssignmentScheme _fold_assignment
public Distribution.Family _distribution
public double _tweedie_power
public java.lang.String[] _ignored_columns
public boolean _ignore_const_cols
public java.lang.String _weights_column
public java.lang.String _offset_column
public java.lang.String _fold_column
public boolean _score_each_iteration
public int _stopping_rounds
public ScoreKeeper.StoppingMetric _stopping_metric
public double _stopping_tolerance
public java.lang.String _response_column
public boolean _balance_classes
public float _max_after_balance_size
public float[] _class_sampling_factors
public int _max_hit_ratio_k
public int _max_confusion_matrix_size
protected double defaultStoppingTolerance()
public final Frame train()
public final Frame valid()
public void read_lock_frames(Job job)
public void read_unlock_frames(Job job)
protected boolean defaultDropNA20Cols()
protected boolean defaultDropConsCols()
public double missingColumnsType()
public boolean hasCheckpoint()
public long checksum()
protected long checksum_impl()