Distort clusters
Bootstrap sampling Stacking Scree plot Targeting Neighborhood function Correlated MAE Centroid stabilize Variance Explained Location Random Forest Latent factors Two- Dimensional ROC Curve Same evaluation metric Within cluster variability Factor structure Uncorrelated Variable- factor relationships
Age Strategy Development Competitive learning Euclidean Rotation First Principal Component Usage Data Weight adaptation Identifiers Error Linear combinations Bagging Overgeneralization Topology Voting ensemble Log loss Initialization Meta- Model Covariance Matrix Shared variance proportion Distance Loadings Validation set Poorly Cross Model comparison Boosting Map Spherical Distort clusters Bootstrap sampling Stacking Scree plot Targeting Neighborhood function Correlated MAE Centroid stabilize Variance Explained Location Random Forest Latent factors Two- Dimensional ROC Curve Same evaluation metric Within cluster variability Factor structure Uncorrelated Variable- factor relationships Age Strategy Development Competitive learning Euclidean Rotation First Principal Component Usage Data Weight adaptation Identifiers Error Linear combinations Bagging Overgeneralization Topology Voting ensemble Log loss Initialization Meta- Model Covariance Matrix Shared variance proportion Distance Loadings Validation set Poorly Cross Model comparison Boosting Map Spherical
(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.
K-Distort clusters
E-Bootstrap sampling
E-Stacking
P-Scree plot
S-Targeting
S-Neighborhood function
F-Correlated
P-MAE
K-Centroid stabilize
P-Variance Explained
S-Location
E-Random Forest
F-Latent factors
S-Two-Dimensional
P-ROC Curve
P-Same evaluation metric
K-Within cluster variability
F-Factor structure
P-Uncorrelated
F-Variable-factor relationships
S-Age
S-Strategy Development
S-Competitive learning
K-Euclidean
F-Rotation
P-First Principal Component
S-Usage Data
S-Weight adaptation
S-Identifiers
F-Error
P-Linear combinations
E-Bagging
S-Overgeneralization
S-Topology
E-Voting ensemble
P-Log loss
K-Initialization
E-Meta-Model
P-Covariance Matrix
F-Shared variance proportion
S-Distance
P-Loadings
P-Validation set
K-Poorly
P-Cross Model comparison
E-Boosting
S-Map
K-Spherical