IsolationForestModel implements IsolationForestModel
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◆ IsolationForestModel() [1/2]
Synapse.ML.Isolationforest.IsolationForestModel.IsolationForestModel |
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◆ IsolationForestModel() [2/2]
Synapse.ML.Isolationforest.IsolationForestModel.IsolationForestModel |
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◆ GetBootstrap()
bool Synapse.ML.Isolationforest.IsolationForestModel.GetBootstrap |
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Gets bootstrap value
- Returns
- bootstrap: If true, draw sample for each tree with replacement. If false, do not sample with replacement.
◆ GetContamination()
double Synapse.ML.Isolationforest.IsolationForestModel.GetContamination |
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Gets contamination value
- Returns
- contamination: The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter.
◆ GetContaminationError()
double Synapse.ML.Isolationforest.IsolationForestModel.GetContaminationError |
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Gets contaminationError value
- Returns
- contaminationError: The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value.
◆ GetFeaturesCol()
string Synapse.ML.Isolationforest.IsolationForestModel.GetFeaturesCol |
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Gets featuresCol value
- Returns
- featuresCol: The feature vector.
◆ GetInnerModel()
JavaTransformer Synapse.ML.Isolationforest.IsolationForestModel.GetInnerModel |
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Gets innerModel value
- Returns
- innerModel: the fit isolation forrest instance
◆ GetMaxFeatures()
double Synapse.ML.Isolationforest.IsolationForestModel.GetMaxFeatures |
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Gets maxFeatures value
- Returns
- maxFeatures: The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.
◆ GetMaxSamples()
double Synapse.ML.Isolationforest.IsolationForestModel.GetMaxSamples |
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Gets maxSamples value
- Returns
- maxSamples: The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.
◆ GetNumEstimators()
int Synapse.ML.Isolationforest.IsolationForestModel.GetNumEstimators |
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Gets numEstimators value
- Returns
- numEstimators: The number of trees in the ensemble.
◆ GetPredictionCol()
string Synapse.ML.Isolationforest.IsolationForestModel.GetPredictionCol |
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Gets predictionCol value
- Returns
- predictionCol: The predicted label.
◆ GetRandomSeed()
long Synapse.ML.Isolationforest.IsolationForestModel.GetRandomSeed |
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Gets randomSeed value
- Returns
- randomSeed: The seed used for the random number generator.
◆ GetScoreCol()
string Synapse.ML.Isolationforest.IsolationForestModel.GetScoreCol |
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Gets scoreCol value
- Returns
- scoreCol: The outlier score.
◆ Load()
◆ Read()
Get the corresponding JavaMLReader instance.
- Returns
- an JavaMLReader<IsolationForestModel> instance for this ML instance.
◆ Save()
void Synapse.ML.Isolationforest.IsolationForestModel.Save |
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Saves the object so that it can be loaded later using Load. Note that these objects can be shared with Scala by Loading or Saving in Scala.
- Parameters
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path | The path to save the object to |
◆ SetBootstrap()
Sets value for bootstrap
- Parameters
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value | If true, draw sample for each tree with replacement. If false, do not sample with replacement. |
- Returns
- New IsolationForestModel object
◆ SetContamination()
IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetContamination |
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Sets value for contamination
- Parameters
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value | The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter. |
- Returns
- New IsolationForestModel object
◆ SetContaminationError()
IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetContaminationError |
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double |
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Sets value for contaminationError
- Parameters
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value | The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value. |
- Returns
- New IsolationForestModel object
◆ SetFeaturesCol()
◆ SetInnerModel()
IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetInnerModel |
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JavaTransformer |
value | ) |
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Sets value for innerModel
- Parameters
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value | the fit isolation forrest instance |
- Returns
- New IsolationForestModel object
◆ SetMaxFeatures()
Sets value for maxFeatures
- Parameters
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value | The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. |
- Returns
- New IsolationForestModel object
◆ SetMaxSamples()
Sets value for maxSamples
- Parameters
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value | The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. |
- Returns
- New IsolationForestModel object
◆ SetNumEstimators()
Sets value for numEstimators
- Parameters
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value | The number of trees in the ensemble. |
- Returns
- New IsolationForestModel object
◆ SetPredictionCol()
IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetPredictionCol |
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Sets value for predictionCol
- Parameters
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value | The predicted label. |
- Returns
- New IsolationForestModel object
◆ SetRandomSeed()
Sets value for randomSeed
- Parameters
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value | The seed used for the random number generator. |
- Returns
- New IsolationForestModel object
◆ SetScoreCol()
The documentation for this class was generated from the following file:
- synapse/ml/isolationforest/IsolationForestModel.cs