generalization and model complexity
54
double descent
Statistics and Machine Learning
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data
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height data
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weight data
hypothesis testing
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one-sample t-test
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independent samples t-test
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statistical power
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the problem with t-test
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permutation test
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numpy vs pandas
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exact vs. Monte Carlo permutation tests
confidence interval
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basic concepts
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analytical confidence interval
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empirical confidence interval
regression
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the geometry of regression
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least squares
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partitioning of the sum of squares
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R-squared
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equivalence
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linear mixed effect model
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logistic regression
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logistic 2d
correlation
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correlation
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correlation and linear regression
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cosine
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significance (p-value)
bayes
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Bayes’ theorem from the ground up
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parametric generative classification
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odds and log likelihood
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logistic connection
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conjugate prior
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the boy-girl paradox
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monty hall
svd and pca
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SVD for image compression
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SVD for regression
decision trees
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CART: classification
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CART: regression
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random forest
information theory
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entropy
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cross-entropy and KL divergence
likelihood
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probability and likelihood
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maximum likelihood estimation
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MLE and summary statistics
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MLE and linear regression
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MLE and information theory
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MLE and regularization
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MLE and classification
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MLE and bayesian inference
generalization and model complexity
47
motivation
48
bias-variance tradeoff
49
overfitting and underfitting
50
cross-validation
51
data splitting
52
regularization
53
when more data changes the tradeoff
54
double descent
neural networks
55
Nielsen’s NNDL, ch.1
56
Nielsen’s NNDL, ch.2
57
Nielsen’s NNDL, ch.3A
58
Nielsen’s NNDL, ch.3B
miscellaneous
59
trend test
60
SHAP values
generalization and model complexity
54
double descent
54
double descent
Code
53
when more data changes the tradeoff
55
Nielsen’s NNDL, ch.1