supervised learning linear regression model classification Occam's razor train error bias- variance tradeoff Gauss- Markov theorem reinforcement learning unsupervised learning train data Python clustering residuals test error underfitting overfitting features test data confidence intervals double descent blackbox learning algorithm labels R squared metric supervised learning linear regression model classification Occam's razor train error bias- variance tradeoff Gauss- Markov theorem reinforcement learning unsupervised learning train data Python clustering residuals test error underfitting overfitting features test data confidence intervals double descent blackbox learning algorithm labels R squared metric
(Print) Use this randomly generated list as your call list when playing the game. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.
B-supervised learning
N-linear regression model
N-classification
I-Occam's razor
N-train error
O-bias-variance tradeoff
G-Gauss-Markov theorem
I-reinforcement learning
O-unsupervised learning
G-train data
B-Python
B-clustering
G-residuals
I-test error
O-underfitting
I-overfitting
G-features
N-test data
O-confidence intervals
I-double descent
B-blackbox
G-learning algorithm
B-labels
O-R squared metric