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’.

Logistic Regression models the probability of a binary or multi-class outcome by applying a logistic function to a linear combination of input features. It tries to minimize the difference between predicted probabilities and actual labels through optimization.

Usage:

Start the algorithm from the Processing Toolbox panel.
Select a training dataset or create one by clicking the processing algorithm icon, then click run.

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
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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
            - field:FIELD_NAME to use a data defined value taken from the FIELD_NAME field
            - expression:SOME EXPRESSION to use a data defined value calculated using a custom QGIS expression
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