Plot confusion matrix sweet

plots a confusion matrix using the ‘yellowbrick’ package.

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

Import the required models and fetch an extreme gradient boosting

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_yb_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 ='xgboost' )
pmo.fit(X, y )
print(pmo.estimator_ ) # pmo.XGB.best_estimator_

XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=1.5, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.07, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=2, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=300, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...)

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

ypred = pmo.predict(Xt)

# now plot the score
plot_yb_confusion_matrix (pmo.XGB.best_estimator_, Xt, yt  )

/home/docs/checkouts/readthedocs.org/user_builds/watex/envs/0.2.4/lib/python3.10/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning:

could not determine class_counts_ from previously fitted classifier

ConfusionMatrix(ax=<AxesSubplot:title={'center':'XGBClassifier Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>,
                cmap=<matplotlib.colors.ListedColormap object at 0x7fec8bd7e950>,
                estimator=XGBClassifier(base_score=None, booster='gbtree',
                                        callbacks=None, colsample_bylevel=None,
                                        colsample_bynode=None,
                                        colsample_bytree=None,
                                        early_stopping_rounds=Non...
                                        grow_policy=None, importance_type=None,
                                        interaction_constraints=None,
                                        learning_rate=0.07, max_bin=None,
                                        max_cat_threshold=None,
                                        max_cat_to_onehot=None,
                                        max_delta_step=None, max_depth=2,
                                        max_leaves=None, min_child_weight=None,
                                        missing=nan, monotone_constraints=None,
                                        n_estimators=300, n_jobs=None,
                                        num_parallel_tree=None,
                                        objective='multi:softprob',
                                        predictor=None, ...))

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