我從零實作 Logistic Regression、Decision Trees、Random Forests 和 K-Means：最令我意外的結果

Available on github https://github.com/hariomlohardev/mini-sklearn

KMeans matched sklearn to the decimal while every classifier trailed.

What I built

minisklearn: five from-scratch models plus hand-rolled metrics, all
stdlib Python, trained on the Titanic dataset (889 rows, 7 features,
~38% survived). Same split throughout (test_ratio=0.2, seed=0), all
scored with my own confusion-matrix metrics.

The surprise

All the other models that i tested against the sklearn library are performing a bit bad than sklearn implementation but the KMeans matched exectly each things no differance
I didn't expect that to happend

The numbers

Same split, same ruler — my models vs scikit-learn:

The pattern: my classifiers systematically under-predict "survived"
(recall 0.29–0.44 vs sklearn's 0.71–0.79)

A too-tiny learning rate on
unscaled features plus exact ID3 splits fragmenting continuous inputs.
KMeans, meanwhile, matches sklearn's inertia to the decimal, which says
the Lloyd loop is exactly right.

What I'd do differently

In future i will follow this models approch if something can be solved by this rather than jumping to Neural networks and gradient decent

I know there are problems that can't be solved by this tiny models but they surpries so much well on some special cases where neutral networks perform bad