Fit KernelRidge

Kernel ridge regression. Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space. The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsilon-insensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at prediction-time.

Usage:

1. Start the algorithm from the Processing Toolbox panel.

2. Select a training dataset or create one by clicking the processing algorithm icon, then click run.

   

Parameters

Regressor [string]

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

Default:

from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.kernel_ridge import KernelRidge

krr = KernelRidge()
param_grid = {'kernel': ['rbf'],
              'gamma': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
              'alpha': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
tunedKRR = GridSearchCV(cv=3, estimator=krr, scoring='neg_mean_absolute_error', param_grid=param_grid)
regressor = make_pipeline(StandardScaler(), tunedKRR)

Training dataset [file]

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

Outputs

Output regressor [fileDestination]

Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitKernelridge:

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

regressor: Regressor
    Default value:  from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.kernel_ridge import KernelRidge

krr = KernelRidge()
param_grid = {'kernel': ['rbf'],
              'gamma': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
              'alpha': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
tunedKRR = GridSearchCV(cv=3, estimator=krr, scoring='neg_mean_absolute_error', param_grid=param_grid)
regressor = make_pipeline(StandardScaler(), tunedKRR)
    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
outputRegressor: Output regressor
    Argument type:  fileDestination
    Acceptable values:
            - Path for new file

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

outputRegressor: <outputFile>
    Output regressor