NaiveBayes¶
Use naive Bayes to classify data instances.
This is a very basic classifier that can work well in situations where one would also use LDA or QDA. This method can handle large numbers of features without problems since it does not account for correlations between those features. Like all machine learning methods, this method needs to be calibrated ("trained") before it can make any predictions on data. For this, the method requires training instances and associated training labels. The typical way to get such labels associated with time-series data is to make sure that a marker stream is included in the data, which is usually imported together with the data using one of the Import nodes, or received over the network alongside with the data, e.g., using the LSL Input node (with a non-empty marker query). These markers are then annotated with target labels using the Assign Targets node. To generate instances of training data for each of the training markers, one usually uses the Segmentation node to extract segments from the continuous time series around each marker. Since this machine learning method is not capable of being trained incrementally on streaming data, the method requires a data packet that contains the entire training data; this training data packet can either be accumulated online and then released in one shot using the Accumulate Calibration Data node, or it can be imported from a separate calibration recording and then spliced into the processing pipeline using the Inject Calibration Data, where it passes through the same nodes as the regular data until it reaches the machine learning node, where it is used for calibration. Once this node is calibrated, the trainable state of this node can be saved to a model file and later loaded for continued use. More Info... Version 1.0.0
Ports/Properties¶
data¶
Data to process.
- verbose name: Data
- default value: None
- port type: DataPort
- value type: Packet (can be None)
- data direction: INOUT
probabilistic¶
Use probabilistic outputs. If enabled, the node will output for each class the probability that a given trial is of that class; otherwise it will output the most likely class label.
- verbose name: Output Probabilities
- default value: True
- port type: BoolPort
- value type: bool (can be None)
initialize_once¶
Calibrate the model only once. If set to False, then this node will recalibrate itself whenever a non-streaming data chunk is received that has both training labels and associated training instances.
- verbose name: Calibrate Only Once
- default value: True
- port type: BoolPort
- value type: bool (can be None)
dont_reset_model¶
Do not reset the model when the preceding graph is changed. Normally, when certain parameters of preceding nodes are being changed, the model will be reset. If this is enabled, the model will persist, but there is a chance that the model is incompatible when input data format to this node has changed.
- verbose name: Do Not Reset Model
- default value: False
- port type: BoolPort
- value type: bool (can be None)
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
- verbose name: Set Breakpoint (Debug Only)
- default value: False
- port type: BoolPort
- value type: bool (can be None)
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.
- verbose name: Metadata
- default value: {}
- port type: DictPort
- value type: dict (can be None)