Plot Precision-Recall (PR)

computes the score based on the decision function

and plot the result as a score vs threshold.

# Author: L.Kouadio
# Licence: BSD-3-clause

from watex.exlib.sklearn import SGDClassifier
from watex.datasets.dload import load_bagoue
from watex.utils import cattarget
from watex.view.mlplot import EvalPlot
X , y = load_bagoue(as_frame =True )
sgd_clf = SGDClassifier(random_state= 42) # our estimator
b= EvalPlot(scale = True , encode_labels=True)
b.fit_transform(X, y)
# binarize the label b.y
ybin = cattarget(b.y, labels= 2 ) # can also use labels =[0, 1]
b.y = ybin
# plot the Precision-recall tradeoff
b.plotPR(sgd_clf , label =1) # class=1

EvalPlot(tname= None, objective= None, scale= True, ... , sns_height= 4.0, sns_aspect= 0.7, verbose= 0)