org.apache.spark.ml.classification
LogisticRegressionModel
Related Docs:
object LogisticRegressionModel
| package classification
org.apache.spark.ml.classification
An alias for getOrDefault().
An alias for getOrDefault().
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown
if trainingSummary == None or it is a multiclass model.
If threshold and thresholds are both set, ensures they are consistent.
If threshold and thresholds are both set, ensures they are consistent.
IllegalArgumentException if threshold and thresholds are not equivalent
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
A vector of model coefficients for "binomial" logistic regression.
A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.
Vector
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params.
Subclasses should implement this method and set the return type properly.
See defaultCopy().
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately.
Default Params are copied from and to defaultParamMap, and explicitly set Params are
copied from and to paramMap.
Warning: This implicitly assumes that this Params instance and the target instance
share the same set of default Params.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
Param for the ElasticNet mixing parameter, in range [0, 1].
Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
Evaluates the model on a test dataset.
Evaluates the model on a test dataset.
Test dataset to evaluate model on.
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance. See explainParam().
extractParamMap with no extra values.
extractParamMap with no extra values.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Param for the name of family which is a description of the label distribution to be used in the model.
Param for the name of family which is a description of the label distribution to be used in the model. Supported options:
Param for features column name.
Param for features column name.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema().
This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
Gets a param by its name.
Gets a param by its name.
Get threshold for binary classification.
Get threshold for binary classification.
If thresholds is set with length 2 (i.e., binary classification),
this returns the equivalent threshold:
1 / (1 + thresholds(0) / thresholds(1))
.
Otherwise, returns threshold if set, or its default value if unset.
1 / (1 + thresholds(0) / thresholds(1))
}}}
Otherwise, returns threshold if set, or its default value if unset.
IllegalArgumentException if thresholds is set to an array of length other than 2.
Get thresholds for binary or multiclass classification.
Get thresholds for binary or multiclass classification.
If thresholds is set, return its value.
Otherwise, if threshold is set, return the equivalent thresholds for binary
classification: (1-threshold, threshold).
If neither are set, throw an exception.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Indicates whether this Model has a corresponding parent.
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.
The model intercept for "binomial" logistic regression.
The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.
Double
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Param for label column name.
Param for label column name.
The lower bounds on coefficients if fitting under bound constrained optimization.
The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.
The lower bounds on intercepts if fitting under bound constrained optimization.
The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
Number of classes (values which the label can take).
Number of classes (values which the label can take).
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
The parent estimator that produced this model.
The parent estimator that produced this model.
For ensembles' component Models, this value can be null.
Predict label for the given feature vector.
Predict label for the given feature vector.
The behavior of this can be adjusted using thresholds.
Predict the probability of each class given the features.
Predict the probability of each class given the features. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
Estimated class conditional probabilities
Raw prediction for each possible label.
Raw prediction for each possible label.
The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
a measure of confidence in each possible label (where larger = more confident).
This internal method is used to implement transform() and output rawPredictionCol.
vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.
Param for prediction column name.
Param for prediction column name.
Given a vector of class conditional probabilities, select the predicted label.
Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.
predicted label
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
Given a vector of raw predictions, select the predicted label.
Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.
predicted label
Non-in-place version of raw2probabilityInPlace()
Non-in-place version of raw2probabilityInPlace()
Estimate the probability of each class given the raw prediction, doing the computation in-place.
Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
Estimated class conditional probabilities (modified input vector)
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault.
Annotating this with varargs can cause compilation failures due to a Scala compiler bug.
See SPARK-9268.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
Set threshold in binary classification, in range [0, 1].
Set threshold in binary classification, in range [0, 1].
If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)).
When setThreshold() is called, any user-set value for thresholds will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
Default is 0.5.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Note: When setThresholds() is called, any user-set value for threshold will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
Param for whether to standardize the training features before fitting the model.
Param for whether to standardize the training features before fitting the model.
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown
if trainingSummary == None.
Param for threshold in binary classification prediction, in range [0, 1].
Param for threshold in binary classification prediction, in range [0, 1].
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
DoubleVectorVector.
input dataset
transformed dataset
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
input dataset
additional parameters, overwrite embedded params
transformed dataset
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
input dataset
the first param pair, overwrite embedded params
other param pairs, overwrite embedded params
transformed dataset
:: DeveloperApi ::
:: DeveloperApi ::
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
The upper bounds on coefficients if fitting under bound constrained optimization.
The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.
The upper bounds on intercepts if fitting under bound constrained optimization.
The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
input schema
whether this is in fitting
SQL DataType for FeaturesType.
E.g., VectorUDT for vector features.
output schema
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.
Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.
For LogisticRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
This also does not save the parent currently.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
Model produced by LogisticRegression.