For this project, I plan to focus on evaluating the performance of a binary classification model using a dataset of penguin observations. The goal is to use the model to predict whether a penguin belongs to the female class. Instead of relying only on the model’s predicted class labels, I plan to analyze the predicted probabilities and examine how different probability thresholds affect the model’s decisions. My approach will begin by exploring the dataset to better understand the distribution of the target variable (sex) and determine whether the classes are balanced. I will also calculate the null error rate to establish a baseline for model performance. Next, I will create new class predictions using probability thresholds of 0.2, 0.5, and 0.8. For each threshold, I will build a confusion matrix to identify true positives, false positives, true negatives, and false negatives. Using these values, I will calculate accuracy, precision, recall, and F1 score. Comparing these results will help illustrate how threshold selection influences classification performance and the tradeoff between false positives and false negatives. One challenge I anticipate is interpreting the relationship between predicted probabilities and predicted classes. Another challenge may be understanding the tradeoffs among different evaluation metrics