AI Exam - Math 1A - Fishing

 

AI Practitioner Exam Prep

Math

The Setup 1

A fisherman in a rocky pond with lots of fish and rocks casting his net.

Math Terms 1

"Accuracy" = of fish and rocks in pond, % correct? successes / total. Classification.

F1 Score = best fisherman. balance between your net being full of fish (Precision) and not leaving fish behind (Recall). measures harmonic mean of precision and recall in Classification. good for binary classification tasks like churn prediction. F1 = 2 x (Precision x Recall) / (Precision + Recall). best for rocky ponds.

Precision = in your net, % that are fish? total positives / total positives + false negatives. Classification. High precision means no junk caught.

Recall = of all fish in pond, % caught? total positives / total true + false positives. Classification. High recall means you didn't miss many fish, even if caught some junk.

The Setup 2

After a while, the fisherman starts guessing the weight of the fish.

Math Terms 2

Mean Squared Error = penalty for big errors. On every error, he has to draw a square on a chalkboard where the sides are the length of his mistake. MSE is the average area of all those squares. measures the avg squared difference between estimated and actual.

Regression = The guessing game. The fisherman looks at a fish in the water and guesses, "That fish weighs 5 lbs." Predicts continuous, numerical output values based on input features, mapping relationships to forecast trends like prices, demand, or temperatures.

Root Mean Squared Error = standard metric for regressions. rather than draw squares, he decides just to measure the length. calcs avg difference between predict and actual then square roots it.