Fit LogisticRegression

Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross-entropy loss if the ‘multi_class’ option is set to ‘multinomial’.

Parameters

Classifier [string]

Scikit-learn python code. See LogisticRegression for information on different parameters.

Default:

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
logisticRegression = LogisticRegression()
classifier = make_pipeline(StandardScaler(), logisticRegression)

Training dataset [file]

Training dataset pickle file used for fitting the classifier. If not specified, an unfitted classifier is created.

Outputs

Output classifier [fileDestination]

Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitLogisticregression:

----------------
Arguments
----------------

classifier: Classifier
    Default value:  from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
logisticRegression = LogisticRegression()
classifier = make_pipeline(StandardScaler(), logisticRegression)
    Argument type:  string
    Acceptable values:
            - String value
dataset: Training dataset (optional)
    Argument type:  file
    Acceptable values:
            - Path to a file
outputClassifier: Output classifier
    Argument type:  fileDestination
    Acceptable values:
            - Path for new file

----------------
Outputs
----------------

outputClassifier: <outputFile>
    Output classifier