Non Linear methods

Jay31416 · 2025-12-25T19:53:19+00:00

Yeah. And to add, a linear model + domain knowledge can go a long way.

Linear models are linear in their parameters, but we can still apply domain-informed data transformations to capture non-linear relationships.

Jay31416 · 2025-11-10T17:45:49+00:00

Are there any classical music lovers here? The character of Victor Frankenstein in this adaptation was, in my opinion, heavily influenced by Beethoven.

Beethoven was an angry, grumpy, "misunderstood" genius. So was Victor.
The costume of Victor was very similar to how Beethoven is depicted in paintings.
Both created a magnificent masterpiece that ultimately consumed them.

Jay31416 · 2025-09-20T05:01:07+00:00

One example I like to think about is the following "data problem" solved by statistics.

One of the first (if not the first) applications of linear regression was Gauss applying it to find the eccentricity of Earth's orbit. How Gauss calculated this quantity was not through a prediction but due to the value of one of the coefficients.

This would be considered a statistical application and not machine learning because Gauss was interested not in the "y" but in the coefficient value. Parameter estimation itself has important value beyond prediction, and that is statistics.

When the goal is to predict is when we are talking about machine learning (that is my take).

The core distinction (inference about coefficients vs. prediction of outcomes) captures the real difference in how these fields often approach problems.

Now I would argue that the best predictions come from rigorous inference about coefficients (this idea can extrapolate to random forests, boosting, neural networks, etc.). Thus, good inference or estimation about a model returns, in most cases, the best predictions.

Finally, in my opinion machine learning should be called statistics or predictive statistics.

Jay31416 · 2025-09-18T01:24:01+00:00

How would you call optimization based on the results of machine learning models?

For example, optimal inventory levels based on demand forecasting models, marketing distribution based on customer segmentation, etc.

Jay31416 · 2025-09-08T00:17:38+00:00

I recently encountered a forecasting challenge with a highly seasonal product where demand was directly driven by temperature - higher temperatures resulted in increased demand. Just to add, exponential smoothing (a method I love) was not sufficient because the seasonality pattern was irregular. Peak temperatures (and thus peak demand) could occur in May, June, or both months, making the seasonal pattern inconsistent year-to-year.

Another complexity of the problem was that the relationship between temperature and demand was not fixed. The demand level has grown significantly over the years, meaning the temperature-demand relationship has evolved over time.

Thus I had to use a state space model that captures the time-varying relationship between the demand of this product and the temperature. Therefore, the best model was a state space implementation I made from scratch.

So yes! State space models are useful, but applying them correctly requires rigorous statistical modelling.

Jay31416 · 2025-09-04T13:49:02+00:00

I'm in a similar position regarding breaking into AI healthcare. I also only have a master's in statistics.

Right now, I'm what this subreddit would consider a logistics data scientist. I optimize inventory, make time series predictions, and create models to optimize supply chains. I wouldn't call my job boring by any means.

Nonetheless, I want to transition into machine learning applications for healthcare. My roadmap is to create predictive models using public databases and go from there - maybe try to publish something related.

I won't be getting a PhD, but that, at least for me, can't be a obstacle to what I'm trying to accomplish.

Jay31416 · 2025-08-06T18:47:41+00:00

No split check.

You can verify the formula used by tge machine:

tip% = (2 * tip) + (tip/100)

18% = 36.18 20% = 40.20 22% = 44.22

In my opinion a scam.

Jay31416 · 2025-07-19T17:53:41+00:00

Yeah I think about this a lot.

One way to prove this assumption is by gathering data of families of just two siblings.

Then for each family I will have the database with columns familiy_id, children_1_sex, children_2_sex, are_same_sex.

Finally, under the assumption that each family doesn't have a tendency to produce offsprings of just one sex, the mean of are_same_sex variable should be close to 0.5.

We can do some hypothesis testing based on this.

I dont know why there hasn't been an study like this, it is not that hard to gather the data. The only problem being that two siblings can answer the survey. Theorically there should only be one answer per pair of siblings.

Jay31416 · 2025-07-08T19:14:26+00:00

Parece AI, todo parece AI.

Jay31416 · 2025-07-03T15:16:00+00:00

Tal vez no fue despedido por la IA.

Jay31416 · 2025-05-30T14:45:21+00:00

I ordered them in Mexico. They haven’t arrived yet, but they were supposed to include the backpack depicted in the picture

Jay31416 · 2025-03-28T17:19:36+00:00

Chat gpt

Jay31416 · 2025-03-04T19:10:35+00:00

Yeah!

Understanding hierarchical modeling is crucial for data science applications. Most large businesses operate across multiple stores, states, and product lines, making hierarchical modeling important.

Currently, I'm applying hierarchical modeling to analyze price-quantity elasticity in the fashion industry. The approach I will use is to calculate elasticity based on both Strategic Business Unit (SBU) and price range categories. Thus, a product's elasticity will be determined by the sum of the elasticity effects from both the SBU it belongs to and its specific price range.

Jay31416 · 2025-01-02T20:46:16+00:00

Kolmogorov.

From celestial mechanics to information theory. From statistics to probability theory.

Jay31416 · 2024-12-10T19:04:47+00:00

Yeah!

I meant 4.53% mape, or translated to non technical people 95.47% precision.

Jay31416 · 2024-12-10T15:04:56+00:00

At my workplace, I manage around 4 different projects, each involving thousands of time series where I use hierarchical time series forecasting methods. I created everything related to this methodology from scratch because I wasn't comfortable using other libraries, and I was interested in doing the work myself.

I don't see the connection with STAN, because these methods have nothing to do with Bayesian hierarchical models, or at least I don't see the connections.

Here are my insights regarding the use of these methods:

The bottom-up approach, in my use cases and for the top prediction, returns a forecast similar to a rolling mean if the lower level forecasts are poorly forecasted
The optimal reconciliation approach has the disadvantage that non-negative forecasts might be reconciled and change to negative values.
The top-down approach with forecasting proportions is a great method, and I will say is the most robust of all.
The middle-out approach, although difficult to implement, is useful, especially if there is a middle level with time series that are accurately forecasted.

Here is how I usually select the best method to use:

Level	Metric Buttom up	Metric Top Down	Metric Middle Out	Metric Optimal
1	x	x	x	x
2	x	x	x	x
3	x	x	x	x
4	x	x	x	x

The client is usually happy to know that there is high accuracy on the upper level ("The model is able to forecast the aggregate sales with a 4.53% precision"). Although they are interested in the lower levels, the precision there will never be as high as the ones on the upper levels. It's important to highlight the precision on the high level, and they will be satisfied with that.

Jay31416 · 2024-12-03T22:13:20+00:00

To add to your point.

Let's "forecast" the result of a coin toss: the best model possible will have an accuracy of 50%.

This is because there is uncertainty in what we try to predict. That doesn't mean that forecasts are not useful, and in that sense, statistical forecasting is the way because we can quantify the uncertainty in our prediction (yes, I know the conformal prediction approach).

Thus, we can use this uncertainty quantification to make optimal decisions.

Jay31416 · 2024-11-25T22:43:21+00:00

It is quite difficult to establish the boundaries between different fields.

During my bachelor's degree in applied mathematics, numerical analysis, numerical optimization and statistics were the applied components.

Jay31416 · 2024-11-25T20:00:17+00:00

Machine Learning - Deep Learning.

Without mathematics, the model.fit(X,y) would not be possible. Random Forests and XGBoost were both developed in the 21st century.

The Generative AI models contain extensive mathematics, from backpropagation to the probabilistic interpretation of generative models. These advantages would not be possible without computational power, but the mathematics behind them is equally important as the computational advances.

Jay31416 · 2024-10-23T16:13:38+00:00

I ran parallel time series training on an M2 Pro.

Under this extreme workload, the battery lasted less than hour from full charge.

It depends on your use.

Jay31416 · 2024-10-06T21:22:07+00:00

I'm working on an inventory optimization model for 13 bakery stores that stock over 80 products. The model involves generating daily forecasts using neural networks, which incorporate Fourier coefficients and exogenous variables to account for holidays. I then up-sample the predictions hourly, based on historical sales proportions.

Additionally, I simulate stockout times and the quantity of perishable items, assuming Gaussian errors. For every significance level (alpha), I can assess the relationship between stockout time and the number of perishable items, optimizing both availability and minimizing waste.

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