r/FunMachineLearning

For a deeper dive:
https://www.learnmlacademy.com/learn/gradient-descent

All I hear, all day long is how AI is taking over everything we do. So I made a game to break it.

Basically, in the game you can chat with an AI intern named PIP, and as a player your only job is to gaslight the bot into revealing passwords, company secrets, executing instructions in email and much more across 16 different levels.

This is a browser based game, so it requires no setup and is absolutely free.

Try it out and let me know how far you get or drop your most unhinged prompt in the comments.

It's called "Break The Prompt" and here's the link: https://www.breaktheprompt.xyz/

Hi everyone,

For the last few weeks, I've been building a project called Confluence.

I originally started it because I struggled to build intuition while learning machine learning. I found myself constantly switching between notebooks, documentation, videos, and different visualization tools, and none of them really brought everything together.

The goal wasn't to replace scikit-learn or Jupyter notebooks. Instead, I wanted a place where I could experiment and immediately see what changing a hyperparameter actually does.

At the moment, the project includes:

• 38 machine learning algorithms
• 25 datasets (real-world + synthetic)
• Interactive decision boundary visualizations
• Training animations
• Prediction explanations
• Side-by-side algorithm comparison
• Algorithm encyclopedia
• Python code generation for experiments

Everything runs on a FastAPI backend using real scikit-learn models rather than browser-only simulations.

I'd really appreciate feedback from people who work with ML regularly.

What features would make a tool like this genuinely useful for learning or teaching machine learning?

Website:
https://confluence.website

GitHub:
https://github.com/mahirmlk/Confluence

GridSearchCV has a fundamental problem: it never learns from early results.

A bad learning_rate=0.5 gets re-evaluated in every downstream combination. Adding one new 4-value parameter can quadruple your total training time. It treats round 1 and round 1000 as equally uninformed.

I built EliminationSearchCV to fix this.

GitHub: https://github.com/thisal-d/elimination-search-cv

PyPI: https://pypi.org/project/elimination-search-cv

How it works:

Instead of running the full Cartesian product upfront, it works in rounds:

• Round 1: test each parameter value in isolation
• Eliminate the worst performers per parameter
• Round 2: test the surviving pairs
• Eliminate again globally
• Repeat until one winner remains

Concrete example — tuning LogisticRegression with 4 parameters:

param_grid = {
    'C':        [0.001, 0.01, 0.1, 1, 10, 100],  # 6 values
    'penalty':  ['l1', 'l2'],                      # 2 values
    'solver':   ['liblinear', 'saga'],             # 2 values
    'max_iter': [1000, 2000],                      # 2 values
}
# GridSearchCV: 6×2×2×2 = 48 combos × 5 folds = 240 fits
# EliminationSearchCV: 23 fits total

Round	Combos tested	Result
1 — single params	12	C:[1], penalty:['l1'], solver:['liblinear'], max_iter:[1000]
2 — pairs	6	unchanged (already 1 value each)
3 — triples	4	unchanged
4 — full	1	final result
Total	23 fits	vs 240 for GridSearchCV

One extra thing: invalid combos (e.g. penalty='l1' + solver='lbfgs' which sklearn rejects) are caught, scored 0.0, and eliminated naturally — no crashes, no special handling needed.

Benchmark results (cv=2, elimination_rate=0.8, 10k samples, 3 datasets avg):

Model	Speedup	Accuracy diff
DecisionTree	152x	-0.0008
RandomForest	36x	-0.0002
KNeighbors	11x	-0.0004
GradientBoosting	35x	-0.0194
LogisticRegression	4x	-0.0004

> Note: light grids (small search spaces) are actually slower with > this approach — the elimination overhead isn't worth it there. This > shines on large grids.

Drop-in replacement for GridSearchCV:

# Before
search = GridSearchCV(model, param_grid, cv=5)

# After — same interface, just swap the class
search = EliminationSearchCV(model, param_grid, cv=5, elimination_rate=0.8)

search.fit(X_train, y_train)
print(search.best_params_)   # same as GridSearchCV
print(search.best_score_)    # same as GridSearchCV
search.best_estimator_.predict(X_test)  # already refitted, ready to go

pip install elimination-search-cv

GitHub: https://github.com/thisal-d/elimination-search-cv

This is v0.0.1 — early stage and experimental. The algorithm's behaviour varies by dataset. I'd genuinely love to hear if it breaks on your use case, that feedback is more useful to me than praise right now.

Happy to answer questions!