Model.plot¶

In [ ]:

Model.plot(max_nb_points: int = 50,
           ax=None, 
           **style_kwds,)

Draws the model.

Parameters¶

Name	Type	Optional	Description
max_nb_points	int	✓	Maximum number of points to display.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

ax : Matplotlib axes object

Example¶

In [3]:

# SVM
from verticapy.learn.svm import LinearSVC
model = LinearSVC(name = "public.SVC_iris")
model.drop()
model.fit("public.iris_clean", 
          ["PetalLengthCm", "PetalWidthCm"], 
          "Species_Iris-setosa")
model.plot()

Out[3]:

<AxesSubplot:xlabel='"PetalLengthCm"', ylabel='"PetalWidthCm"'>

In [6]:

# Logit
from verticapy.learn.linear_model import LogisticRegression
model = LogisticRegression(name = "public.SVC_titanic")
model.drop()
model.fit("public.titanic", 
          ["fare", "age"], 
          "survived")
model.plot()

Out[6]:

<Axes3DSubplot:xlabel='"fare"', ylabel='"age"'>