To those, who want to understand agents properly

There is a post (in Russian) from a guy I really respect. Formally, it’s an advertisement for Yet Another Agent’s channel — but this time it’s not about news or hype. It’s about fundamentals.

What I especially like: the post contains a curated list of publications worth reading first, before drowning in frameworks, tools, and buzzwords.

From uplift to logit

Where we are
In the previous post we started to build a bridge from uplift to a score, which lets us solve binary classification problem. What we achieved:
⚗️ calculated probabilities for all combinations of binary feature f and target L
⚗️ wrote a score expression for S as logarithm of conditional probabilities fraction

Notation
⚗️ L - a binary target, 0 or 1 values (l in picture)
⚗️ r - a binary feature, 0 or 1 values
⚗️ α = P(L=1) - probability of target to be 1 or average value of target
⚗️ ϐ = P(f=1) - coverage, probability of feature to be equal 1, or average value of feature
⚗️ γ = P(L=1|f=1)/P(l=1) - uplift, gain in target we have when selected subset with f = 1
⚗️ S - score for ideal model to predict target

Problem statement
For given α, ϐ, γ:
🦐 create a dataset with non-stationary uplift γ
🦐 create baseline S for target prediction

Increment
For me these conditional probabilities are terra incognita. I don't have solid intuition, what is right and what is wrong. So, I was wanted a small sanity check for all this complex for me math. And test is quite simple: γ=1 means that factor doesn't give new information about target. In this case weights for this factor should become 0. In the previous attempt it wasn't obvious at all.

This time I slightly changed notation and used conditional probabilities instead of absolute one. And it is totally obvious now that when γ=1, expressions under logarithms become 1, logarithms are equal to 0 and therefore, expressions with N and M (number of ones and zeroes in sets of factors) goes away. It means that factors are not important, exactly what we assumed taking γ=1. Now I'm quite confident in this result, so let's dive deeper.

Outcomes
🦐 we have watertight expressions for dataset generation for arbitrary γ
🦐 there is an expression for the optimal model S
🦐 case γ=1 checked and S behave as it expected
🦐 expression for S is a bridge from probabilities to logistic regression model
🦐 it’s a universal approach — we can try it in the wild

Titanic - Maching Learning from Disaster

I want to close my gestalt. I invented a new ML method, I'm working as ML engineer for 9 years, I taught ML in GoTo school and in Russian Armenian University. But I have never submitted Titanic from Kaggle. Let's try to build our own approach. I think it's always interesting to start from scratch, build your own solution and only then read work of others.

Kaggle
It's a famous platform for machine learning. There is plethora of datasets for different tasks. You can find datasets for regression, classification, ranking, whatever. Kaggle provides free GPU resources for training your models, it allows you to check the quality of your solutions, compete with best ML geeks of our planet. "Grandmaster of Kaggle" is the title you want to have in your CV. So, not a bad place to hang around.

Titanic
13 years ago (what a number!) The Competition was published. Nowadays it's a good tradition to start ML lessons with this dataset. Why? Because:
🛳 It's a binary classification task
🛳 you can train your grasp of precision, accuracy, recall, ROC, ROC AUC
🛳 a lot of missing values
🛳 multimodal data (strings + numbers)

Data
On the data page one can check dataset's structure. It's split on two parts: training part and test part. You are to train model on your data, then apply the model to test data and submit it. Platform will check your results and give you feedback. Let's check fields:

target
☠️ survived - target field, 1 for survived (38%), 0 otherwise.

features
🛂 PassengerId — Unique identifier for each passenger. Not used as a predictor.
🏛Pclass — Ticket class: 1 = 1st (upper), 2 = 2nd (middle), 3 = 3rd (lower). Proxy for socio-economic status.
👺Name — Passenger name. Can be used to extract title (Mr, Mrs, Miss, etc.) or family.
👧🏻 Sex — Biological sex (male / female). Strong predictor of survival (“women and children first”).
🧙🏿‍♂️ Age — Age in years. Fractional if under 1. Has missing values.
👩‍❤️‍👨 SibSp — Number of siblings or spouses aboard (Sibling + Spouse). Discrete count (0–8).
👨‍👩‍👦 Parch — Number of parents or children aboard (Parent + Child). Discrete count (0–6).
🎫 Ticket — Ticket number. Often alphanumeric; can be messy for modeling.
💲 Fare — Passenger fare paid. Continuous; may correlate with Pclass and Cabin.
🏡 Cabin — Cabin number. Heavily missing; when present, deck/position may be informative.
🧳 Embarked — Port of embarkation: C = Cherbourg, Q = Queenstown, S = Southampton.

EDA

It's always a good idea to check your data. You can understand, how full they are and how informative they are.

🏛Pclass

No missing values, three values with frequencies:
1 216
2 184
3 491

We can calculate average survival rate for all three classes:
1 63%
2 47%
3 24%

Now we can recall that average survival rate is 38%. So, first class highly votes for the survival, second class slightly votes for survival and third class highly votes against survival.

👺Name

It's a good quiestion, whether name contains some information for survival at all. Let's check. I want to use hash trick technique and figure out. Experiment with hash trick demonstrated that Name feature contains information and gives 62% ROC AUC. So I dove into this field deeper. It turned out that it has format like

Futrelle, Mrs. Jacques Heath (Lily May Peel)

I checked distribution of survived for different titles and it is a good feature:

Master 0.575000 40
Miss 0.702703 185
Mr 0.156673 517
Other 0.724832 149

👧🏻 Sex
definitely a strong feature, it is on the picture

🧳 Embarked — Port of embarkation: C = Cherbourg, Q = Queenstown, S = Southampton.
Three different values, strong uplift => valueable feature

I'm going to continue this exploration. I would like to see that this topic is interesting. Please vote with 🔥 if you like it.

Jupyter Lab
github: eda

Old but gold

Let's discuss a programming task. You have a bunch of numeric pairs like 5, 5, 7, 7, 2, 2 and one unique number, like 13. You mix everything into one array and shuffle it. Now you have something like [7, 2, 13, 5, 2, 7, 5]. Let n be the number of elements and let it be huge, like 100000000. Numbers are 32- or 64-bit wide. The task is to find the unique number. In our case it would be 13.

Feel free to share in comments time and memory complexities of your approach to this problem. It would be like T=O(...); M=O(...)

I think it would be fun to collect options for a few days and then compare algorithms.

How to vibecode Chizhik-Pyzchic app

In the alternative channel there is step-by-step instruction how to create a small, but fully functional web application. Post language is Russian.

Old but gold

There seems to be two different problems. But really, it's the same problem.

1. Find the repeating and non-repeating parts of the decimal expansion of 1/n.
1/3 = 0.(3)
1/6 = 0.1(6)
2. For a singly linked list, check whether it contains a cycle.

Share your thoughts and ideas in the comments. Use the “spoiler” feature wisely. I’ll publish the canonical approach in 3 days, if needed.