def show(self, view=None, model_type='classification'):
"""
show(view=None)
Visualize a Classifier model with GraphLab Create
:mod:`~graphlab.canvas`.
This function starts Canvas if it is not already running.
If the Model has already been plotted, this function will update the
plot.
Parameters
----------
view : str, optional
The name of the Model view to show. Can be one of:
- <Type>: <Description>
Returns
-------
view : graphlab.canvas.view.View
An object representing the GraphLab Canvas view
See Also
--------
canvas
Examples
--------
Suppose 'm' is a Model, we can view it in GraphLab Canvas using:
>>> m.show()
"""
from graphlab.visualization.show import show
show(self, view=view, model_type=model_type)
def show(self, view=None, model_type='regression'):
"""
show(view=None)
Visualize with GraphLab Canvas :mod:`~graphlab.canvas`.
This function starts Canvas if it is not already running.
If the Model has already been plotted, this function will update the plot.
Parameters
----------
view : str, optional
The name of the Model view to show. Can be one of:
- *Summary*: The summary description of a Model.
- *Evaluation*: A visual representation of the evaluation results for
a Model.
Returns
-------
view : graphlab.canvas.view.View
An object representing the GraphLab Canvas view
See Also
--------
canvas
Examples
--------
Suppose 'm' is a Model, we can view it in GraphLab Canvas using:
>>> m.show()
"""
from graphlab.visualization.show import show
show(self, view=view, model_type=model_type)
def show(self, view=None, model_type='regression'):
"""
show(view=None)
Visualize with GraphLab Canvas :mod:`~graphlab.canvas`.
This function starts Canvas if it is not already running.
If the Model has already been plotted, this function will update the plot.
Parameters
----------
view : str, optional
The name of the Model view to show. Can be one of:
- *Summary*: The summary description of a Model.
- *Evaluation*: A visual representation of the evaluation results for
a Model.
Returns
-------
view : graphlab.canvas.view.View
An object representing the GraphLab Canvas view
See Also
--------
canvas
Examples
--------
Suppose 'm' is a Model, we can view it in GraphLab Canvas using:
>>> m.show()
"""
from graphlab.visualization.show import show
show(self, view=view, model_type=model_type)
def show(self, view=None, model_type='classification'):
"""
show(view=None)
Visualize a Classifier model with GraphLab Create
:mod:`~graphlab.canvas`.
This function starts Canvas if it is not already running.
If the Model has already been plotted, this function will update the
plot.
Parameters
----------
view : str, optional
The name of the Model view to show. Can be one of:
- <Type>: <Description>
Returns
-------
view : graphlab.canvas.view.View
An object representing the GraphLab Canvas view
See Also
--------
canvas
Examples
--------
Suppose 'm' is a Model, we can view it in GraphLab Canvas using:
>>> m.show()
"""
from graphlab.visualization.show import show
show(self, view=view, model_type=model_type)
def show(self, view=None, model_type='base'):
"""
show(view=None)
Visualize with GraphLab Canvas :mod:`~graphlab.canvas`.
This function starts Canvas if it is not already running.
If the Model has already been plotted, this function will update the plot.
Parameters
----------
view : str, optional
The name of the Model view to show. Can be one of:
- Summary: Shows the statistics of the training process such as size of the data and time cost. The summary also shows the parameters and settings for the model training process if available.
- Evaluation: Shows precision recall plot as line chart. Tooltip is provided for pointwise analysis. Precision recall values are shown in the tooltip at any given cutoff value the mouse points to.
Parameters
----------
view : str, optional
- 'Summary': The summary description of a Model.
- 'Evaluation': A visual representation of the evaluation results for
a Model.
Returns
-------
view : graphlab.canvas.view.View
An object representing the GraphLab Canvas view
See Also
--------
canvas
Examples
--------
Suppose 'm' is a Model, we can view it in GraphLab Canvas using:
>>> m.show()
"""
from graphlab.visualization.show import show
show(self, view=view, model_type=model_type)
