BayesianRidgeRegression¶
Estimate a continuous output value from features using Bayesian Ridge Regression.
Bayesian Ridge regression is a an elegant Bayesian method to learn a linear mapping between input data and desired output values from training data, which is closely related to ridge regression. The main difference to ridge regression is that in ridge regression, there is a tunable parameter that controls how strongly the method should be regularized. This parameter controls effectively how flexible or complex the solution may be, to prevent over-fitting to random details of the data and therefore to improve the generalization to new data. In ridge regression, this parameter is tuned using cross-validation, that is, by empirical testing on held-out data. In the Bayesian variant, there is such a parameter as well, however, the optimal degree of regularization is estimated from the data as well in a theoretically clean and principled fashion. This method assumes that both inputs and outputs are Gaussian distributed, that is, have no or very few major statistical outliers. If the output follows a radically different distribution, for instance between 0 and 1, or nonnegative, or discrete values, then different methods may be more appropriate (for instance, classification methods for discrete values). To ameliorate the issue of outliers in the data, the raw data can be cleaned of artifacts with various artifact removal methods. To the extent that the assumptions hold true, this method is highly competitive with other linear methods. 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 1.1.1
Ports/Properties¶
data¶
Data to process.
max_iter¶
Maximum number of iterations. This is one of the stopping criteria to limit the compute time. The default is usually fine, and gains from increasing the number of iterations will be minimal (it can be worth experimenting with lower iteration numbers if the algorithm must finish in a fixed time budget, at a cost of potentially less accurate solutions).
tolerance¶
Convergence tolerance. This is the desired errors tolerance or acceptable inaccuracy in the solution. Using larger values gives less accurate results, but will lead to faster compute times. Note that, for biosignal-driven machine learning systems, one often does not need very small tolerances.
verbosity¶
Verbosity level. Higher numbers will produce more extensive diagnostic output.
include_bias¶
Include a bias term. If false, your features need to be centered, or include a dummy feature set to 1.
normalize_features¶
Normalize features. Should only be disabled if the data comes with a predictable scale (e.g., normalized in some other way).
alpha_shape¶
Alpha shape parameter. This is only included for completeness and usually does not have to be adjusted. This is the shape parameter for the Gamma distribution prior over the alpha parameter. By default, this is an uninformative prior.
alpha_rate¶
Alpha rate parameter. This is only included for completeness and usually does not have to be adjusted. This is the rate parameter for the Gamma distribution prior over the alpha parameter. By default, this is an uninformative prior.
lambda_shape¶
Lambda shape parameter. This is only included for completeness and usually does not have to be adjusted. This is the shape parameter for the Gamma distribution prior over the lambda parameter. By default, this is an uninformative prior.
lambda_rate¶
Lambda rate parameter. This is only included for completeness and usually does not have to be adjusted. This is the rate parameter for the Gamma distribution prior over the lambda parameter. By default, this is an uninformative prior.
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.
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.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.