Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.vertica.decomposition.MCA.contour

MCA.contour(nbins: int = 100, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure

Draws the model’s contour plot.

Parameters

nbins: int, optional

Number of bins used to discretize the two predictors.

chart: PlottingObject, optional

The chart object to plot on.

**style_kwargs

Any optional parameter to pass to the Plotting functions.

Returns

obj

Plotting Object.

Examples

Let’s consider we’ve fitted a model model.

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour()

Important

Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.