clustering labels overfitting learning algorithm test error residuals linear regression model confidence intervals R squared metric Gauss- Markov theorem test data bias- variance tradeoff Python features supervised learning double descent train data train error classification underfitting reinforcement learning blackbox Occam's razor unsupervised learning clustering labels overfitting learning algorithm test error residuals linear regression model confidence intervals R squared metric Gauss- Markov theorem test data bias- variance tradeoff Python features supervised learning double descent train data train error classification underfitting reinforcement learning blackbox Occam's razor unsupervised learning
(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-clustering
B-labels
I-overfitting
G-learning algorithm
I-test error
G-residuals
N-linear regression model
O-confidence intervals
O-R squared metric
G-Gauss-Markov theorem
N-test data
O-bias-variance tradeoff
B-Python
G-features
B-supervised learning
I-double descent
G-train data
N-train error
N-classification
O-underfitting
I-reinforcement learning
B-blackbox
I-Occam's razor
O-unsupervised learning