Stacking Poorly Over- generalization Bagging Location Centroids Stabilize Two- dimensional Competitive learning Validation set Weight adaptation Variance Explained Factor Structure Bootstrap Sampling Covariance Matrix Rotation Variable- factor relationships Same evaluation metric First principal component Strategy Development Mean Absolute Error Log loss Distance Random Forest Linear Combinations Spherical Identifiers Distorts clusters Boosting Error Voting Ensemble Loadings ROC Curve Within- cluster variability Shared variance proportion Latent Factors F1 Score Cross- model comparison Meta- model Age Uncorrelated Scree Plot Euclidian Neighborhood function Initialization MAP Targeting Topology Usage Data Stacking Poorly Over- generalization Bagging Location Centroids Stabilize Two- dimensional Competitive learning Validation set Weight adaptation Variance Explained Factor Structure Bootstrap Sampling Covariance Matrix Rotation Variable- factor relationships Same evaluation metric First principal component Strategy Development Mean Absolute Error Log loss Distance Random Forest Linear Combinations Spherical Identifiers Distorts clusters Boosting Error Voting Ensemble Loadings ROC Curve Within- cluster variability Shared variance proportion Latent Factors F1 Score Cross- model comparison Meta- model Age Uncorrelated Scree Plot Euclidian Neighborhood function Initialization MAP Targeting Topology Usage Data
(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.
E-Stacking
k-Poorly
S-Over-generalization
E-Bagging
S-Location
k-Centroids Stabilize
S-Two-dimensional
S-Competitive learning
M-Validation set
S-Weight adaptation
P-Variance Explained
F-Factor Structure
E-Bootstrap Sampling
P-Covariance Matrix
F-Rotation
F-Variable-factor relationships
M-Same evaluation metric
P-First principal component
S-Strategy Development
M-Mean Absolute Error
M-Log loss
S-Distance
E-Random Forest
P-Linear Combinations
k-Spherical
S-Identifiers
k-Distorts clusters
E-Boosting
F-Error
E-Voting Ensemble
P-Loadings
M-ROC Curve
k-Within-cluster variability
F-Shared variance proportion
F-Latent Factors
M-F1 Score
M-Cross-model comparison
E-Meta-model
S-Age
P-Uncorrelated
P-Scree Plot
k-Euclidian
S-Neighborhood function
k-Initialization
S-MAP
S-Targeting
S-Topology
S-Usage Data