Multivariate Pattern Analysis in Python

# mvpa2.misc.plot.base.plot_decision_boundary_2d

`mvpa2.misc.plot.base.``plot_decision_boundary_2d`(dataset, clf=None, targets=None, regions=None, maps=None, maps_res=50, vals=None, data_callback=None)

  Plot a scatter of a classifier’s decision boundary and data points

  Assumes data is 2d (no way to visualize otherwise!!)

  Parameters:

  dataset : `Dataset`

  > Data points to visualize (might be the data `clf` was train on, or any novel data).

  clf : `Classifier`, optional

  > Trained classifier

  targets : string, optional

  > What samples attributes to use for targets. If None and clf is provided, then `clf.params.targets_attr` is used.

  regions : string, optional

  > Plot regions (polygons) around groups of samples with the same attribute (and target attribute) values. E.g. chunks.

  maps : string in {‘targets’, ‘estimates’}, optional

  > Either plot underlying colored maps, such as clf predictions within the spanned regions, or estimates from the classifier (might not work for some).

  maps_res : int, optional

  > Number of points in each direction to evaluate. Points are between axis limits, which are set automatically by matplotlib. Higher number will yield smoother decision lines but come at the cost of O^2 classifying time/memory.

  vals : array of floats, optional

  > Where to draw the contour lines if maps=’estimates’

  data_callback : callable, optional

  > Callable object to preprocess the new data points. Classified points of the form samples = data_callback(xysamples). I.e. this can be a function to normalize them, or cache them before they are classified.