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Definitions (36)
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Block-averaging
Averaging all samples recorded during a block of continuous stimulation in a block-design fMRI experiment. The rationale behind this technique is, that averaging might lead to an improved signal-to-no [..]
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Classifier
A model that maps an arbitrary feature space into a discrete set of labels.
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Meta-classifier
An internal to PyMVPA term to describe a classifier which is usually a proxy to the main classifier which it wraps to provide additional data preprocessing (e.g. feature selection) before actually tra [..]
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Cross-validation
A technique to assess the generalization of the constructed model by the analysis of accuracy of the model predictions on presumably independent dataset.
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Chunk
A chunk is a group of samples. In PyMVPA chunks define independent groups of samples (note: the groups are independent from each other, not the samples in each particular group). This information is i [..]
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Conditional Attribute
An attribute of a learner which might be enabled or disabled, grouped within .ca attributes collection. If enabled, it might cause additional computation and memory consumption, so the “heaviest” cond [..]
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Confusion matrix
Visualization of the generalization performance of a classifier. Each row of the matrix represents the instances in a predicted class, while each column represents the samples in an actual (target) cl [..]
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Dataset
In PyMVPA a dataset is the combination of samples, and their Dataset attributes.
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Dataset attribute
An arbitrary auxiliary information that is stored in a dataset.
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decoding
This term is usually used to refer to the application of machine learning or pattern recognition techniques to brainimaging datasets, and therefore is another term for MVPA. Sometimes also ‘brain-read [..]
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