[Verse 1]
When your model seems perfect on training day
But test data makes it fall away
That's the bias-variance playing games with you
Let me break down what you need to do
Bias is the distance from the truth we seek
Variance shows how much predictions leak
When we change the data that we train upon
The decomposition keeps us strong
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Verse 2]
Stratified keeps your proportions right
Grouped CV when clusters bite
Time series splits respect the order
Never peek across that border
K-fold standard, leave-one-out
But what's your validation route about?
The strategy depends on data type
Don't fall for the random hype
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Bridge]
Log loss punishes the overconfident mind
Calibration plots show if probabilities align
Precision-recall when classes are skewed
AUC-ROC when balanced data's viewed
McNemar's test for classification pairs
Bootstrap intervals show the confidence we wear
Paired t-test when metrics are continuous
Statistical testing keeps us rigorous
[Verse 3]
Regularization adds a penalty term
L1 makes sparse features firm
L2 keeps the weights from growing wild
Lambda controls how much we've styled
Grid search or random for the hyperparameter dance
Cross-validation gives each setting a chance
The bias-variance tradeoff guides our hand
Too simple or complex, we must understand
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Outro]
Model selection's an art and science combined
With proper evaluation, truth you'll find
From bias-variance to statistical tests
These tools will help you do your best