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).

Imports#

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

Generate data & model#

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#

Confusion Matrix Report


Total running time of the script: ( 0 minutes 0.118 seconds)

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