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