Course Content

Recognizing Handwritten Digits

# Confusion Matrix

In **machine learning**, a **confusion matrix** is a critical tool utilized for assessing the performance of a **classification model**. This matrix effectively encapsulates the model's predictions, juxtaposing them against the actual outcomes.

Within **scikit-learn**, the creation of a confusion matrix is facilitated by the `confusion_matrix`

function, housed in the `sklearn.metrics`

module. This function demands two pivotal inputs: the **true labels** and the **predicted labels**, yielding a square matrix where rows and columns align with these labels.

The core of the confusion matrix comprises four key values: **true positives (TP)**, **false positives (FP)**, **true negatives (TN)**, and **false negatives (FN)**. These values are instrumental in quantifying the model's predictive accuracy, delineating between correct and erroneous forecasts.

Furthermore, this matrix is foundational in computing critical metrics such as **accuracy**, **precision**, **recall**, and the **F1 score**. For instance, **accuracy** is derived from the formula: **(TP + TN) / (TP + TN + FP + FN)**.

Task

**Generate a confusion matrix** using the `ConfusionMatrixDisplay`

class from `sklearn.metrics`

, with the true test labels and the predicted labels as inputs.

Task

**Generate a confusion matrix** using the `ConfusionMatrixDisplay`

class from `sklearn.metrics`

, with the true test labels and the predicted labels as inputs.

Everything was clear?

In **machine learning**, a **confusion matrix** is a critical tool utilized for assessing the performance of a **classification model**. This matrix effectively encapsulates the model's predictions, juxtaposing them against the actual outcomes.

Within **scikit-learn**, the creation of a confusion matrix is facilitated by the `confusion_matrix`

function, housed in the `sklearn.metrics`

module. This function demands two pivotal inputs: the **true labels** and the **predicted labels**, yielding a square matrix where rows and columns align with these labels.

The core of the confusion matrix comprises four key values: **true positives (TP)**, **false positives (FP)**, **true negatives (TN)**, and **false negatives (FN)**. These values are instrumental in quantifying the model's predictive accuracy, delineating between correct and erroneous forecasts.

Furthermore, this matrix is foundational in computing critical metrics such as **accuracy**, **precision**, **recall**, and the **F1 score**. For instance, **accuracy** is derived from the formula: **(TP + TN) / (TP + TN + FP + FN)**.

Task

**Generate a confusion matrix** using the `ConfusionMatrixDisplay`

class from `sklearn.metrics`

, with the true test labels and the predicted labels as inputs.