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Confusion Matrix Report#
This notebook provides an overview for using and understanding the Confusion Matrix Report check.
Structure:
What is the Confusion Matrix Report?#
The ConfusionMatrixReport
produces a confusion matrix visualization which summarizes the
performance of the model. The confusion matrix contains the TP (true positive), FP (false positive),
TN (true negative) and FN (false negative), from which we can derive the relevant metrics,
such as accuracy, precision, recall etc. (confusion matrix).
Generate data & model#
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import ConfusionMatrixReport
iris = load_iris(as_frame=True)
clf = AdaBoostClassifier()
frame = iris.frame
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
clf.fit(X_train, y_train)
ds = Dataset(pd.concat([X_test, y_test], axis=1),
features=iris.feature_names,
label='target')
Run the check#
check = ConfusionMatrixReport()
result = check.run(ds, clf)
result.show()
Define a condition#
We can define our check a condition that will validate if all the misclassified
cells/samples in the confusion matrix is below a certain threshold. Using the
misclassified_samples_threshold
argument, we can specify what percentage of the total samples
our condition should use to validate the misclassified cells.
# Let's add a condition and re-run the check:
check = ConfusionMatrixReport()
check.add_condition_misclassified_samples_lower_than_condition(misclassified_samples_threshold=0.2)
result = check.run(ds, clf)
result.show()
#%%
Total running time of the script: (0 minutes 0.194 seconds)