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