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