Tag: How to Estimate Autoregressive Parameters?

Posted on

What are the top 3 methods used to find Autoregressive Parameters in Data Science?

What are the top 3 methods used to find Autoregressive Parameters in Data Science?

What are the top 3 methods used to find Autoregressive Parameters in Data Science?

 In order to find autoregressive parameters, you will first need to understand what autoregression is. Autoregression is a statistical method used to create a model that describes data as a function of linear regression of lagged values of the dependent variable. In other words, it is a model that uses past values of a dependent variable in order to predict future values of the same dependent variable.

In time series analysis, autoregression is the use of previous values in a time series to predict future values. In other words, it is a form of regression where the dependent variable is forecasted using a linear combination of past values of the independent variable. The parameter values for the autoregression model are estimated using the method of least squares.

The autoregressive parameters are the coefficients in the autoregressive model. These coefficients can be estimated in a number of ways, including ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO). Once estimated, the autoregressive parameters can be used to predict future values of the dependent variable.

To find the autoregressive parameters, you need to use a method known as least squares regression. This method finds the parameters that minimize the sum of the squared residuals. The residual is simply the difference between the predicted value and the actual value. So, in essence, you are finding the parameters that best fit the data.

What are the top 3 methods used to find Autoregressive Parameters in Data Science?
What are the top 3 methods used to find Autoregressive Parameters in Data Science?

How to Estimate Autoregressive Parameters?


There are three main ways to estimate autoregressive parameters: ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO).

Ordinary Least Squares: Ordinary least squares is the simplest and most common method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values.

Maximum Likelihood: Maximum likelihood is another common method for estimating autoregressive parameters. This method estimates the parameters by maximizing the likelihood function. The likelihood function is a mathematical function that quantifies the probability of observing a given set of data given certain parameter values.

Least Squares with L1 Regularization: Least squares with L1 regularization is another method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values while also penalizing models with many parameters. L1 regularization penalizes models by adding an extra term to the error function that is proportional to the sum of absolute values of the estimator coefficients.

Finding Autoregressive Parameters: The Math Behind It
To find the autoregressive parameters using least squares regression, you first need to set up your data in a certain way. You need to have your dependent variable in one column and your independent variables in other columns. For example, let’s say you want to use three years of data to predict next year’s sales (the dependent variable). Your data would look something like this:

| Year | Sales |
|——|——-|
| 2016 | 100 |
| 2017 | 150 |
| 2018 | 200 |

Next, you need to calculate the means for each column. For our sales example, that would look like this:

$$ \bar{Y} = \frac{100+150+200}{3} = 150$$

Now we can calculate each element in what’s called the variance-covariance matrix:

$$ \operatorname {Var} (X)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)^{2} $$

and

$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right) $$

Ace the 2024 AWS Certified Machine Learning Specialty Exam with Flying Colors

For our sales example, that calculation would look like this:

$$ \operatorname {Var} (Y)=\sum _{i=1}^{3}\left({y_{i}}-{\bar {y}}\right)^{2}=(100-150)^{2}+(150-150)^{2}+(200-150)^{2})=2500 $$

and

$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{3}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right)=(2016-2017)(100-150)+(2017-2017)(150-150)+(2018-2017)(200-150))=-500 $$

Now we can finally calculate our autoregressive parameters! We do that by solving this equation:

$$ \hat {\beta }=(X^{\prime }X)^{-1}X^{\prime }Y=\frac {1}{2500}\times 2500\times (-500)=0.20 $$\.20 . That’s it! Our autoregressive parameter is 0\.20 . Once we have that parameter, we can plug it into our autoregressive equation:

$$ Y_{t+1}=0\.20 Y_t+a_1+a_2+a_3footnote{where $a_1$, $a_2$, and $a_3$ are error terms assuming an AR(3)} .$$ And that’s how you solve for autoregressive parameters! Of course, in reality you would be working with much larger datasets, but the underlying principles are still the same. Once you have your autoregressive parameters, you can plug them into the equation and start making predictions!.

Which Method Should You Use?
The estimation method you should use depends on your particular situation and goals. If you are looking for simple and interpretable results, then Ordinary Least Squares may be the best method for you. If you are looking for more accurate predictions, then Maximum Likelihood or Least Squares with L1 Regularization may be better methods for you.

Autoregressive models STEP BY STEP:

1) Download data: The first step is to download some data. This can be done by finding a publicly available dataset or by using your own data if you have any. For this example, we will be using data from the United Nations Comtrade Database.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

2) Choose your variables: Once you have your dataset, you will need to choose the variables you want to use in your autoregression model. In our case, we will be using the import and export values of goods between countries as our independent variables.

3) Estimate your model: After choosing your independent variables, you can estimate your autoregression model using the method of least squares. OLS estimation can be done in many statistical software packages such as R or STATA.

4) Interpret your results: Once you have estimated your model, it is important to interpret the results in order to understand what they mean. The coefficients represent the effect that each independent variable has on the dependent variable. In our case, the coefficients represent the effect that imports and exports have on trade balance. A positive coefficient indicates that an increase in the independent variable leads to an increase in the dependent variable while a negative coefficient indicates that an increase in the independent variable leads to a decrease in the dependent variable.

5)Make predictions: Finally, once you have interpreted your results, you can use your autoregression model to make predictions about future values of the dependent variable based on past values of the independent variables.

Conclusion: In this blog post, we have discussed what autoregression is and how to find autoregressive parameters. 

Estimating an autoregression model is a relatively simple process that can be done in many statistical software packages such as R or STATA.

In statistics and machine learning, autoregression is a modeling technique used to describe the linear relationship between a dependent variable and one more independent variables. To find the autoregressive parameters, you can use a method known as least squares regression which minimizes the sum of squared residuals. This blog post also explains how to set up your data for calculating least squares regression as well as how to calculate Variance and Covariance before finally calculating your autoregressive parameters. After finding your parameters you can plug them into an autoregressive equation to start making predictions about future events!

We have also discussed three different methods for estimating those parameters: Ordinary Least Squares, Maximum Likelihood, and Least Squares with L1 Regularization. The appropriate estimation method depends on your particular goals and situation.

Machine Learning For Dummies
Machine Learning For Dummies

Machine Learning For Dummies App

Machine Learning For Dummies  on iOs:  https://apps.apple.com/us/app/machine–learning-for-dummies-p/id1610947211

Machine Learning For Dummies on Windows: https://www.microsoft.com/en-ca/p/machine–learning-for-dummies-ml-ai-ops-on-aws-azure-gcp/9p6f030tb0mt?

Machine Learning For Dummies Web/Android on Amazon: https://www.amazon.com/gp/product/B09TZ4H8V6

What are some good datasets for Data Science and Machine Learning?

Machine Learning Engineer Interview Questions and Answers

Machine Learning Breaking News 

Transformer – Machine Learning Models

transformer neural network

Machine Learning – Software Classification

Autoregressive Model

Autoregressive generative models can estimate complex continuous data distributions such as trajectory rollouts in an RL environment, image intensities, and audio. Traditional techniques discretize continuous data into various bins and approximate the continuous data distribution using categorical distributions over the bins. This approximation is parameter inefficient as it cannot express abrupt changes in density without using a significant number of additional bins. Adaptive Categorical Discretization (ADACAT) is proposed in this paper as a parameterization of 1-D conditionals that is expressive, parameter efficient, and multimodal. A vector of interval widths and masses is used to parameterize the distribution known as ADACAT. Figure 1 showcases the difference between the traditional uniform categorical discretization approach with the proposed ADACAT.

Each component of the ADACAT distribution has non-overlapping support, making it a specific subfamily of mixtures of uniform distributions. ADACAT generalizes uniformly discretized 1-D categorical distributions. The proposed architecture allows for variable bin widths and more closely approximates the modes of two Gaussians mixture than a uniformly discretized categorical, making it highly expressive than the latter. Additionally, a distribution’s support is discretized using quantile-based discretization, which bins data into groups with similar measured data points. ADACAT uses deep autoregressive frameworks to factorize the joint density into numerous 1-D conditional ADACAT distributions in problems with more than one dimension. 

Continue reading | Check out the paper and github link.

Pytorch – Computer Application

https://torchmetrics.readthedocs.io/en/stable//index.html

Best practices for training PyTorch model

What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?

What are some good datasets for Data Science and Machine Learning?

Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

Machine Learning Engineer Interview Questions and Answers

  • Interning at no-name Startup vs. at proper Tier 1 Org?
    by /u/anotheraccount97 (Data Science) on July 26, 2024 at 1:44 am

    I'm pursuing an MS in Data Science at Columbia University right now, but due to reasons even I can't comprehend, I almost didn't apply for any internships at all. Though I did score straight A's and literally topped Algorithms and NLP somehow without studying, and am generally good and experienced in Deep Learning and applied AI research skills, I still thought I'd not land or clear interviews, and didn't bother applying. I didn't have an internship until late June, and I finally got one through in-person networking at Startup events during NYC Tech Week. Had I applied normally like everyone else, I could've landed Tier 1 companies, prestigious startups and AI firms, like almost everyone else in my cohort. All my friends are interning with the Llama team at Meta, at Microsoft research labs, Nvidia, Tesla, etc. or Top hedge funds in NYC. Most of them will also be getting sponsored return offers with high salaries - they have been stress free since the last year. I will still suffer the wrath of finding a real job until end of this year until I graduate (already very little time left, and I still don't feel like applying). The silver lining is, the startup I've joined is pretty early stage, and I fit in really well with the highly experienced CEO/CTO and learn from him all day. I'm the only intern and I get a lot of attention and hands-on guidance. I'm also getting to see how to build startups from scratch, so it could help me form perspectives later. He said he would 100% hire and sponsor me, but that's only possible if he raises funding and survives. I'm working on cutting edge LLM Agents, RAGs and stuff and it is indeed adding some valuable experience to my resume. But it's more applied, engineering oriented, and not really (applied) AI Research, what I was supposed to be doing with skills I acquired during my MS. But I do think that I should learn MLE and general engineering stuff to be relevant in the industry, especially since I have 2 YOE before the degree, so it may even be expected. However, I am worried that I'm basically interning at a no-name startup which can spoil my career image. Thing is, I do mandatorily need a very high paying and sponsored job to pay back my student debts. What should I do? submitted by /u/anotheraccount97 [link] [comments]

  • Am I hurting my career with this Internship? What should I do?
    by /u/anotheraccount97 (Data Science) on July 26, 2024 at 1:38 am

    I'm pursuing an MS in Data Science at Columbia University right now, but due to reasons even I can't comprehend, I almost didn't apply for any internships at all. Though I did score straight A's and literally topped Algorithms and NLP somehow without studying, and am generally good and experienced in Deep Learning and AI research skills, I still thought I'd not land or clear interviews, and didn't bother applying. I didn't have an internship until late June, and I finally got one through in-person networking at Startup events during NYC Tech Week. Had I applied normally like everyone else, I could've landed Tier 1 companies, prestigious startups and AI firms, like almost everyone else in my cohort. All my friends are interning with the Llama team at Meta, at Microsoft research labs, Nvidia, Tesla, etc. or Top hedge funds in NYC. Most of them will also be getting sponsored return offers with high salaries - they have been stress free since the last year. I will still suffer the wrath of finding a real job until end of this year until I graduate (already very little time left, and I still don't feel like applying). The silver lining is, the startup I've joined is pretty early stage, and I fit in really well with the highly experienced CEO/CTO and learn from him all day. I'm the only intern and I get a lot of attention and hands-on guidance. I'm also getting to see how to build startups from scratch, so it could help me form perspectives later. He said he would 100% hire and sponsor me, but that's only possible if he raises funding and survives. I'm working on cutting edge LLM Agents, RAGs and stuff and it is indeed adding some valuable experience to my resume. But it's more applied, engineering oriented, and not really AI Research, what I was supposed to be doing with skills I acquired during my MS. But I do think that I should learn MLE and general engineering stuff to be relevant in the industry, especially since I have 2 YOE before the degree, so it may even be expected. However, I am worried that I'm basically interning at a no-name startup which can spoil my career image. Thing is, I do mandatorily need a very high paying and sponsored job to pay back my student debts. What should I do? submitted by /u/anotheraccount97 [link] [comments]

  • What's the most interesting Data Science interview question you've encountered?
    by /u/NickSinghTechCareers (Data Science) on July 26, 2024 at 12:42 am

    What's the most interesting Data Science Interview question you've been asked? Bonus points if it: appears to be hard, but is actually easy appears to be simple, but is actually nuanced I'll go first – at a geospatial analytics startup, I was asked about how we could use location data to help McDonalds open up their next store location in an optimal spot. It was fun to riff about what features I'd use in my analysis, and potential downsides off each feature. I also got to show off my domain knowledge by mentioning some interesting retail analytics / credit-card spend datasets I'd also incorporate. This impressed the interviewer since the companies I mentioned were all potential customers/partners/competitors (it's a complicated ecosystem!). How about you – what's the most interesting Data Science interview question you've encountered? submitted by /u/NickSinghTechCareers [link] [comments]

  • Seeking ML Solutions for Analyzing Player Movement in Field Sports
    by /u/vaalenz (Data Science) on July 25, 2024 at 10:40 pm

    Hi everyone! I'm working on a project where I have detailed information on player movements in field sports such as Soccer, Rugby, and Field Hockey. The dataset includes second-by-second data on player positions (latitude and longitude), speed, and heart rate. I’m looking for help with two specific objectives using machine learning: Detecting and Classifying Game Phases: I want to develop a system that can identify and classify different game phases like attacking, defending, counter-attacks, rest periods, etc. Automatically Splitting the Game into Quarters or Halves: Additionally, I need to automatically segment the game into quarters or halves, and determine the exact times these segments occur. I’d appreciate any suggestions on how to approach these problems. What algorithms or models would be best suited for these tasks? Are there any existing frameworks or tools that could be particularly useful?Thanks for your help!! submitted by /u/vaalenz [link] [comments]

  • What is it with jobs requiring a master’s AND a PhD?
    by /u/Rare_Art_9541 (Data Science) on July 25, 2024 at 8:11 pm

    I was looking through some postings On indeed. And I noticed that there are several data science postings that require both a master’s and a PhD. You’re telling me if you decide to skip a master’s and go straight for the PhD, you’re not considered qualified? submitted by /u/Rare_Art_9541 [link] [comments]

  • Does every new data science job get boring after a couple years?
    by /u/Lamp_Shade_Head (Data Science) on July 25, 2024 at 7:26 pm

    I am in my second job after grad school and am noticing a pattern in how I feel about my job as time goes on. In my first job, I felt bored and restless around the 1.5-year mark and eventually left. Now, after 2.5 years at my current job, I find myself feeling bored and disinterested again. I fulfill my responsibilities and do them well, but I no longer go above and beyond as I did in the first year. Is it unusual to feel this way, or is it normal? submitted by /u/Lamp_Shade_Head [link] [comments]

  • Using Kriging interpolation on 2D slices of 3D data
    by /u/RandomFactChecker_ (Data Science) on July 25, 2024 at 6:52 pm

    Hello, I'm working on interpolating 2D data slices from a 3D grid at a specific point. If I take slices around that point in various directions, can I average the kriging values from these different slices to obtain a single overall 3D kriging value for that point? Would doing this ruin the values outputted by the Kinging interpolation and make them have greater error? If I can take this approach what would be the best approach to ensure accuracy in this interpolation process? I am also doing this because I have a working 2D kriging add on for MATLAB that has good documentation but cant find a good one for 3D submitted by /u/RandomFactChecker_ [link] [comments]

  • Worth it to take a pay cut for the data scientist title?
    by /u/son_of_tv_c (Data Science) on July 25, 2024 at 3:17 pm

    I have an MS in stats and 7 years as an analyst under my belt. I've been looking for a data scientist jb ever since I got the MS 4 years ago (got it part time while I started as an analyst) and have been having a hell of a time at it. I get plenty of interest in analyst positoins, but little interest in data scientist positoins. As I'm sure you all know, there is considerable overlap between the titles but HR drones and ATS doesn't necessarily know this. All they care about is key words. I've been offered a data scientist positoin at a company that I am ready to accept. The positoin is a little underpaid for a DS but about enough for me right now, but I'm thinking it could be a great stepping stone. I work that for 2-3 years then I'm competitive for higher compensated DS positoins. However I just got off the phone with a recriuter for a DA positoin that would pay between 25-40k more than the DS positoin (it's just a band at this point). The responsibilities are similar, it's just that this place has more money and is located in a HCOL are (both are remote though so COL and relocating are not a factor for me). More money now would be great, but I don't really know if this is going to leave me in a better position in a few years. Obviously, we're talking an offer vs just one phone screen, the higher DA positoin isn't a sure thing right now. But I'm just wondering if you guys would even keep pursing the DA positoin or just take the DS positoin and make up the difference in a few years with a higher paid DS positoin? Also I hate that this is a factor but I've done 12 interveiws just this month, I really REALLY don't want to do anymore, so it's a huge factor in me wanting to just drop out of the DA interveiw process and take the DS. submitted by /u/son_of_tv_c [link] [comments]

  • Forecast time series model
    by /u/uraz5432 (Data Science) on July 25, 2024 at 1:00 pm

    Hello, I am new to forecasting, looking for suggestions on what model/ models to use for my use case. I have time series data on free trial signups for users to our product. Mainly two categories of users: US users and non US users. The users have unlimited free trial. They can convert to full paid customers anytime if they want to get all the features. We have 50% of the users convert to paid within the first month of the trial, 10% in month 2, and so on. By 4th month of free trial sign up, the conversion to paid is around 1%, and this is then a non zero value for as far as the data goes back ( let’s say 15 months from trial sign up). I have last two years of data for the free trial by each segment. I am using a simple linear model with seasonally for the month to forecast 12 months out for the free trial orders. How can I use the above to forecast the conversion to paid by month for next 12 months? Like mentioned above, the conversions can happen between month 1 to month 15 from the trial sign up date. I would need to forecast US and non US separately. What would be some models to try? Any suggestions on forecasting trials and paid conversions are appreciated. Thanks in advance. submitted by /u/uraz5432 [link] [comments]

  • How do you describe your job to others who don’t know?
    by /u/Rare_Art_9541 (Data Science) on July 25, 2024 at 12:53 pm

    I always struggle with describing what I do without overcomplicating it. Especially with my parents. They speak Spanish and what I try to describe in Spanish I can’t communicate it. submitted by /u/Rare_Art_9541 [link] [comments]

  • Rio: WebApps in pure Python – Technical Description
    by /u/Sn3llius (Data Science) on July 24, 2024 at 2:04 pm

    Hey everyone! Last month we recieved a lot of encouraging feedback from you and used it to improve our framework. First up, we've completely rewritten how components are laid out internally.This was a large undertaking and has been in the works for several weeks now - and the results are looking great! We're seeing much faster layout times, especially for larger (1000+ component) apps. This was an entirely internal change, that not only makes Rio faster, but also paves the way for custom components, something we've been wanting to add for a while. From all the feedback the most common question we've encountered is, "How does Rio actually work?" The following topics have already been detailed in our wiki for the technical description: What are components? How does observing attributes work? How does Diffing, and Reconciliation work? We are working technical descriptions for: How does the Client-Server Communication work? How does our Layouting work? Thanks and we are looking forward to your feedback! 🙂 GitHub submitted by /u/Sn3llius [link] [comments]

  • Why do people want access to llama 3.1 400B?
    by Data Science on July 24, 2024 at 10:46 am

    I've been in analytics engineering for several years now, just now starting to learn the basics of LLM and machine learning, so NLP and stuff like that. I got my hands on llama 3 local on my Windows PC. There's a community of people who are also getting access to llama 3.1 400 billion parameters. The size of it alone to download it is about 800 GB, at least. Like, I'm not joking when I say that. 800 GB downloads size. I don't even know how much RAM would be required to run this but I've heard people say it requires about 256 GB RAM or VRAM... Question is, why do people want this? Does anyone know or can explain that to me? Why would anyone want something so insanely massive, on their local PC? [link] [comments]

  • Drinking From a Fire Hose (First 100 Days & Beyond Question)
    by /u/11FoxtrotCharlie (Data Science) on July 23, 2024 at 11:03 pm

    How do you ensure success for employees that you are bringing into the organization? As more and more organizations realize the potential of data science and big data in general, new departments and roles are created to start adding value utilizing the mass amounts of data available. I have been working with data for years, I am well versed in databases, analytics, programming, and architecture. The one thing I haven't been able to excel in is how to set up an organization for growth by providing a way to easily onboard an analyst. There was a thread a few days ago asking why on-boarding is so disorganized and it made me realize that it is because data is disorganized. Or at least, it has been at the majority of companies I have worked and consulted for. Real world example: A global client of mine utilizes 10 different SaaS services in North America, and an additional 10 globally. All of these SaaS services have APIs and they are accessed through those. Now, they have built automations and have other flows/processes in place utilizing legacy software. These all reside either in the cloud, through admin portals, on virtual machines, or running off SQL stored procedures/SSIS flows. When I walked in, to process map the current state was an exercise in patience: "How does this process run?" "On the server" "OK... what server? what is it called? what sources does it hit?" In your organizations: How do you catalog all of the automated processes and flows? How do you document where these flows and processes are stored, saved, run from? How do you provide documentation on all of your data sources and what APIs you are using? - I don't want a data catalog, I want a Readers Digest Family Handyman Guide to Data. Does anyone have a recommendation that a new hire could sit in front of for a week or less and be able to access/understand/follow/reference from there with success? How can I compile all processes that are running and publish a guide for someone coming aboard? I feel like this subject is so often overlooked, but I imagine that it can create an organization that will thrive and grow faster than one where you have to chase senior employees down and hope they remember (or hope that they didn't lose the source code after re-imaging their pc). And no, a saved Word Document in a Shared Folder on a Shared Drive (or the File tab in Microsoft Teams) is not what I think is best practices. submitted by /u/11FoxtrotCharlie [link] [comments]

  • Having problem with langchain loader
    by /u/Longjumping_Ad_7053 (Data Science) on July 23, 2024 at 8:37 pm

    I have the data in JSON format I’m trying to use the jsonloader but apparently I need a download and import a jq module and that’s where my problem is. I have pip installed jq but when it’s time to import it, I get a no module error and yes it’s installed in venv that I am working in. Has anyone had this problem before submitted by /u/Longjumping_Ad_7053 [link] [comments]

  • New Data science jobs in the NFL, Formula1 and sports analytics companies
    by /u/fark13 (Data Science) on July 23, 2024 at 8:14 pm

    Hey guys, I'm constantly checking for jobs in the sports and gaming analytics industry. I've posted recently in this community and had some good comments. I run www.sportsjobs.online, a job board in that niche. In the last month I added around 200 jobs: I'm celebrating I automated all the NFL teams with this post and doing so I've found a few interesting data science and analytics jobs. Sr. Manager, Data Science @ Houston Texans Data Engineer @ Jacksonville Jaguars CRM Analyst, Business Solutions @ Arizona Cardinals Full Stack Software Engineer @ Tennessee Titans NFL Data Scientist @ Swish Analytics F1 Software Engineer - Vehicle Dynamics @ Williams Racings Software Engineer @ Haas F1 Senior Data & Strategy Analyst @ McLaren There are multiple more jobs related to data science, engineering and analytics in the job board. I've created also a reddit community where I post recurrently the openings if that's easier to check for you. I hope this helps someone! submitted by /u/fark13 [link] [comments]

  • Text classification using LLMs
    by /u/tinkerpal (Data Science) on July 23, 2024 at 7:36 pm

    I want to use LLMs to classify color descriptions into 8 different colors. I tried using text embeddings from these color descriptions and applied cosine similarity to classify them, but the performance was not satisfactory. When I use prompts specifying that it’s a color classifier, it gives correct responses. Is there a way to effectively use embeddings for this use case? The dataset is large, so prompt engineering is not a viable option. submitted by /u/tinkerpal [link] [comments]

  • Is there a place to learn where people aren't petty and condescending?
    by Data Science on July 23, 2024 at 4:19 pm

    I see people posting in this subreddit frequently trying to learn things, asking for recommendations and tips, trying to discuss data science, and about 50% of the replies here are people who think they are so much smarter than they are being petty, mocking them, being denigrating to them, aggressive, toxic, for no reason at all. Just acting like they think they are one of the smartest people in the world to ever exist.. The other 50% are pretty nice, they talk, provide recommendations, support, words of encouragement, advice, technical information. Some wondering if there is another place where people go to discuss data science as they are learning it. I'm not talking about doing a boot camp, or doing a udemy course or anything like that. I'm talking about a place where people who are devoted to learning data science and machine learning fundamentals can go to discuss freely. [link] [comments]

  • If you peek in your AB tests, you're setting yourself up for dissapointment
    by /u/__compactsupport__ (Data Science) on July 23, 2024 at 1:37 pm

    Peeking (looking for significance in an AB test before the experiment has enough samples to reach desired power) is a “no no”. Rationales for not peeking typically mention inflated type 1 error rate. Unless you’re just randomizing into two groups and not changing anything, the null is unlikely to be true. So inflated type one error rate is really not the primary concern. Rather, if we peek then we are setting ourselves up for disappointment. Detected effects from peeking will typically not generalize, and we will be overstating out impact. The reason why is fairly clear when considering the Winner’s Curse. I write a short little blog post to demonstrate just how exaggerated the effects detected from peeking can be here. If you need to tell your stakeholders not to peek, its probably best to come at it from this angle as opposed to a statistical angle, which they neither understand nor care about. submitted by /u/__compactsupport__ [link] [comments]

  • Suggested literature/techniques to model forward moving averages
    by /u/Brites_Krieg (Data Science) on July 22, 2024 at 2:10 pm

    I want to start a personal project, but i'm failing to formulate my business problem into a model. I would love inputs on how to better look into this issue and what type of models/techniques i should be researching to tackle it. I want to model the nth day forward moving average of a metric on a given date based on previous days and on the latest available forward moving average for that given day. For example: Consider today is day 30 and I want to predict up to 360d forward moving average a metric. I will only have the actual average value of the 360d forward moving average on day 360. Currently i have the actual average values for 1d to 30d. I also have all of these forward moving averages for the past 5 years. The goal is to define ranges in for all forward moving averages from the latest date (31d) to 360d. I am failing to think of the type of model i'd be looking for or how should i structure the problem, given how the goal here is not to predict a single value, but all the values in the 31d to 360d range. submitted by /u/Brites_Krieg [link] [comments]

  • Easiest way to calculate required sample size for A/B tests
    by /u/vastava_viz (Data Science) on July 22, 2024 at 2:03 pm

    I am a data scientist that monitors ~5-10 A/B experiments in a given month. I've used numerous online sample size calculators, but had minor grievances with each of them.. so I did a completely sane and normal thing, and built my own! Screenshot of A/B Test calculator at www.samplesizecalc.com/proportion-metric Unlike other calculators, mine can handle different split ratios (e.g. 20/80 tests), more than 2 testing groups beyond "Control" and "Treatment", and you can choose between a one-sided or two-sided statistical test. Most importantly, it outputs the required sample size and estimated duration for multiple Minimum Detectable Effects so you can make the most informed estimate (and of course you can input your own custom MDE value!). Here is the calculator: https://www.samplesizecalc.com/proportion-metric And here is an article explaining the methodology, inputs and the calculator's underlying formula: https://www.samplesizecalc.com/blog/how-sample-size-calculator-works Please let me know what you think! I'm looking for feedback from those who design and run A/B tests in their day-to-day. I've built this to tailor my own needs, but now I want to make sure it's helpful to the general audience as well 🙂 Note: You all were very receptive to the first version of this calculator I posted, so wanted to re-share now that's it's been updated in some key ways. Cheers! submitted by /u/vastava_viz [link] [comments]

 

Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

List of Freely available programming books - What is the single most influential book every Programmers should read



#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks

Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic

Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.

Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.

Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.

Turn your dream into reality with Google Workspace: It’s free for the first 14 days.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes:
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6 96DRHDRA9J7GTN6
63F733CLLY7R7MM
63F7D7CPD9XXUVT
63FLKQHWV3AEEE6
63JGLWWK36CP7WM
63KKR9EULQRR7VE
63KNY4N7VHCUA9R
63LDXXFYU6VXDG9
63MGNRCKXURAYWC
63NGNDVVXJP4N99
63P4G3ELRPADKQU
With Google Workspace, Get custom email @yourcompany, Work from anywhere; Easily scale up or down
Google gives you the tools you need to run your business like a pro. Set up custom email, share files securely online, video chat from any device, and more.
Google Workspace provides a platform, a common ground, for all our internal teams and operations to collaboratively support our primary business goal, which is to deliver quality information to our readers quickly.
 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE
C37HCAQRVR7JTFK
C3AE76E7WATCTL9
C3C3RGUF9VW6LXE
C3D9LD4L736CALC
C3EQXV674DQ6PXP
C3G9M3JEHXM3XC7
C3GGR3H4TRHUD7L
C3LVUVC3LHKUEQK
C3PVGM4CHHPMWLE
C3QHQ763LWGTW4C
Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it. (Email us for more codes)