<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
¿Qué es una matriz de confusión en el aprendizaje automático?<end_of_turn>
<start_of_turn>model
A confusion matrix is a tool used in machine learning to visualize the performance of an algorithm, typically a classification algorithm. It is a table with two dimensions ("Actual" and "Predicted"), and each dimension has a set of categories which should match in both. The matrix compares the actual target values with those predicted by the machine learning model, providing insight into the number of correct and incorrect predictions made by the model. Here's a breakdown of its components:

True Positives (TP): These are cases in which the model correctly predicted the positive class.

True Negatives (TN): These are cases in which the model correctly predicted the negative class.

False Positives (FP): These are cases in which the model incorrectly predicted the positive class (also known as a "Type I error").

False Negatives (FN): These are cases in which the model incorrectly predicted the negative class (also known as a "Type II error").<end_of_turn><eos>