Plot confusion matrix

plots a confusion matrix for a single classifier model.

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

Import the required models and fetch a an Ababoost model

for instance then plot the confusion metric

import matplotlib.pyplot as plt
plt.style.use ('classic')
from watex.datasets import fetch_data
from watex.exlib.sklearn import train_test_split
from watex.models import pModels
from watex.utils.plotutils import plot_confusion_matrix
# split the  data . Note that fetch_data output X and y
X, Xt, y, yt  = train_test_split (* fetch_data ('bagoue analysed'), test_size =.25  )
# train the model with the best estimator
pmo = pModels (model ='ada' )
pmo.fit(X, y )
print(pmo.estimator_ )

/home/docs/checkouts/readthedocs.org/user_builds/watex/envs/0.3.1/lib/python3.10/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning:

The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.

AdaBoostClassifier(estimator=DecisionTreeClassifier(criterion='entropy',
                                                    max_depth=7),
                   learning_rate=0.06)

Predict the score using under the hood the best estimator for adaboost classifier

ypred = pmo.predict(Xt)

# now plot the score
plot_confusion_matrix (yt , ypred )

array([[25,  4,  0,  1],
       [ 9, 16,  3,  2],
       [ 1,  0,  7,  4],
       [ 0,  1,  2, 11]])