generalization and model complexity
53
when more data changes the tradeoff
Statistics and Machine Learning
home
sources
data
1
height data
2
weight data
hypothesis testing
3
one-sample t-test
4
independent samples t-test
5
statistical power
6
the problem with t-test
7
permutation test
8
numpy vs pandas
9
exact vs. Monte Carlo permutation tests
confidence interval
10
basic concepts
11
analytical confidence interval
12
empirical confidence interval
regression
13
the geometry of regression
14
least squares
15
partitioning of the sum of squares
16
R-squared
17
equivalence
18
linear mixed effect model
19
logistic regression
20
logistic 2d
correlation
21
correlation
22
correlation and linear regression
23
cosine
24
significance (p-value)
bayes
25
Bayes’ theorem from the ground up
26
parametric generative classification
27
odds and log likelihood
28
logistic connection
29
conjugate prior
30
the boy-girl paradox
31
monty hall
svd and pca
32
SVD for image compression
33
SVD for regression
decision trees
34
CART: classification
35
CART: regression
36
random forest
information theory
37
entropy
38
cross-entropy and KL divergence
likelihood
39
probability and likelihood
40
maximum likelihood estimation
41
MLE and summary statistics
42
MLE and linear regression
43
MLE and information theory
44
MLE and regularization
45
MLE and classification
46
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
53
when more data changes the tradeoff
53
when more data changes the tradeoff
Code
52
regularization
54
double descent