#89 Preping for AWS Certified Machine Learning — Specialty (MLS-C01) Exam

1.False Positive (FP): This occurs when the model predicts a positive outcome (e.g., the presence of a condition) when it’s actually negative (e.g., the condition is not present). In medical terms, this could mean a healthy person being diagnosed with a disease.

False Negative (FN): This happens when the model predicts a negative outcome (e.g., no condition) when it’s actually positive (e.g., the condition is present). In a medical context, this could mean a person with a disease being told they are healthy.

If count of False Positive is greater than count of False Negative, cost / penalty for company is more when False Negative are predicted.

Accuracy: Accuracy is a measure of how many predictions the model got right, both true positives (TP) and true negatives (TN), divided by the total number of predictions. The formula for accuracy is:

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Recall=TP / (TP+FN) measures how well we capture the postives

Precision=TP / (TP+FP) gets a penalty for FP

2. Collaborative Filtering: It’s a technique commonly used in recommendation systems that analyzes user behavior and preferences to make recommendations. It can be user-based or…