Compute and store patterns from linear models.
The linear model coefficients (filters) are used to extract discriminant neural sources from the measured data. This class computes the corresponding patterns of these linear filters to make them more interpretable [1].
None
A linear model from scikit-learn with a fit method
that updates a coef_
attribute.
If None the model will be LogisticRegression.
Notes
New in version 0.10.
References
Methods
Compute distance from the decision function of y from X. |
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Estimate the coefficients of the linear model. |
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Fit the data and transform it using the linear model. |
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Get parameters for this estimator. |
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Compute predictions of y from X. |
Compute probabilistic predictions of y from X. |
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Score the linear model computed on the given test data. |
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Set the parameters of this estimator. |
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Transform the data using the linear model. |
Estimate the coefficients of the linear model.
Save the coefficients in the attribute filters_
and
computes the attribute patterns_
.
LinearModel
Returns the modified instance.
Examples using fit
:
Linear classifier on sensor data with plot patterns and filters
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
dict
Parameters.
The object.
mne.decoding.LinearModel
#Linear classifier on sensor data with plot patterns and filters