Model.contour¶

In [ ]:

Model.contour(nbins: int = 100, 
              pos_label: (int, float, str) = None, 
              ax=None, 
              **style_kwds,)

Draws the model's contour plot. Only available for regressors, binary classifiers, and for models of exactly two predictors.

Parameters¶

Name	Type	Optional	Description
nbins	int	✓	Number of bins used to discretize the two input numerical vcolumns.
pos_label	int / float / str	✓	Label to consider as positive. All the other classes will be merged and considered as negative for multiclass classification.
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]:

# XGBOOST
from verticapy.learn.ensemble import XGBoostClassifier
model = XGBoostClassifier("xgb_titanic",)
model.drop()
model.fit("public.titanic", 
          ["age", "fare",],
          "survived")
model.contour()

Out[3]:

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

In [5]:

# RandomForest
from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier("rf_titanic",)
model.drop()
model.fit("public.titanic", 
          ["age", "fare",],
          "survived")
model.contour()

Out[5]:

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

In [4]:

# NearestCentroid
from verticapy.learn.neighbors import NearestCentroid
model = NearestCentroid("neighbors_titanic",)
model.drop()
model.fit("public.titanic", 
          ["age", "fare",],
          "survived")
model.contour()

Out[4]:

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

In [5]:

# KNN
from verticapy.learn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier("neighbors_titanic",)
model.drop()
model.fit("public.titanic", 
          ["age", "fare",],
          "survived")
model.contour()

Out[5]:

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

In [3]:

# NaiveBayes
from verticapy.learn.naive_bayes import NaiveBayes
model = NaiveBayes("nb_titanic",)
model.drop()
model.fit("public.titanic", 
          ["age", "fare",],
          "survived")
model.contour()

Out[3]:

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