#multiple_linear_regression نتائج البحث

Subtle but essential: the difference between autocorrelation and multicollinearity in linear regression assumptions > Autocorrelation: residuals are correlated across observations, often over time. Violates the independence assumption and leads to unreliable standard errors. >…

TheGlobalMinima's tweet image. Subtle but essential: the difference between autocorrelation and multicollinearity in linear regression assumptions
> Autocorrelation: residuals are correlated across observations, often over time. Violates the independence assumption and leads to unreliable standard errors.
>…
TheGlobalMinima's tweet image. Subtle but essential: the difference between autocorrelation and multicollinearity in linear regression assumptions
> Autocorrelation: residuals are correlated across observations, often over time. Violates the independence assumption and leads to unreliable standard errors.
>…

Linear Regression is one of the most important tools in a Data Scientist's toolbox. Yet it's super confusing for beginners. Let's fix that: 🧵

mdancho84's tweet image. Linear Regression is one of the most important tools in a Data Scientist's toolbox.

Yet it's super confusing for beginners. 

Let's fix that: 🧵

Machine Learning: Regression Matrices-- -> Means Absolute Error -> Mean Square Error -> Root Mean Square Error -> R2 Score -> Adjusted R2 Score

mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score

Hola 🤠 Comparto paso a paso cómo realizar un modelo #multinivel en #R (y un modelo #nulo) Hace casi un año publiqué en @RevPolitica donde lo utilicé, y quise dejar este ejemplo para quienes inician con estos modelos 🔗 github.com/lesflores/mode… Incluye datos👩‍💻 #CódigoDeTodxs

lesssflo's tweet image. Hola 🤠 Comparto paso a paso cómo realizar un modelo #multinivel en #R (y un modelo #nulo)

Hace casi un año publiqué en @RevPolitica donde lo utilicé, y quise dejar este ejemplo para quienes inician con estos modelos

 🔗 github.com/lesflores/mode…

Incluye datos👩‍💻

#CódigoDeTodxs

Multiple Linear Regression exemplified for dummies👇🏻

rfeers's tweet image. Multiple Linear Regression exemplified for dummies👇🏻

Multiple-class Logistic Regression clearly explained 👇🏻

rfeers's tweet image. Multiple-class Logistic Regression clearly explained 👇🏻

Multiple-class Logistic Regression clearly explained 👇🏻

rfeers's tweet image. Multiple-class Logistic Regression clearly explained 👇🏻

Why and how does gradient/matrix orthogonalization work in Muon for training #LLMs? We introduce an isotropic curvature model to explain it. Take-aways: 1. Orthogonalization is a good idea, "on the right track". 2. But it might not be optimal. [1/n]

weijie444's tweet image. Why and how does gradient/matrix orthogonalization work in Muon for training #LLMs?

We introduce an isotropic curvature model to explain it. Take-aways:

1. Orthogonalization is a good idea, "on the right track".

2. But it might not be optimal. [1/n]

Machine Learning: - Linear Regression: Simple Linear Regression

mili_booo's tweet image. Machine Learning:
- Linear Regression: Simple Linear Regression
mili_booo's tweet image. Machine Learning:
- Linear Regression: Simple Linear Regression
mili_booo's tweet image. Machine Learning:
- Linear Regression: Simple Linear Regression
mili_booo's tweet image. Machine Learning:
- Linear Regression: Simple Linear Regression

Linear Regression is a fundamental algorithm in supervised Machine Learning used for predictive modeling. Learn more about it here 🧵 👇

daansan_ml's tweet image. Linear Regression is a fundamental algorithm in supervised Machine Learning used for predictive modeling.

Learn more about it here 🧵 👇

Many, many matrices : ML/AI Very, very large matrices: Quantum Mechanics Many, many, very, very large matrices: Quantum field theory Learn linear algebra (and functional analysis)!


Continuing our IMO-gold journey, I’m delighted to share our #EMNLP2025 paper “Towards Robust Mathematical Reasoning”, which tells some of the key stories behind the success of our advanced Gemini #DeepThink at this year IMO. Finding the right north-star metrics was highly…

lmthang's tweet image. Continuing our IMO-gold journey, I’m delighted to share our #EMNLP2025 paper “Towards Robust Mathematical Reasoning”, which tells some of the key stories behind the success of our advanced Gemini #DeepThink at this year IMO. Finding the right north-star metrics was highly…

Very excited to share that an advanced version of Gemini Deep Think is the first to have achieved gold-medal level in the International Mathematical Olympiad! 🏆, solving five out of six problems perfectly, as verified by the IMO organizers! It’s been a wild run to lead this…

lmthang's tweet image. Very excited to share that an advanced version of Gemini Deep Think is the first to have achieved gold-medal level in the International Mathematical Olympiad! 🏆, solving five out of six problems perfectly, as verified by the IMO organizers! It’s been a wild run to lead this…


Multiple Linear Regression exemplified for dummies👇🏻

rfeers's tweet image. Multiple Linear Regression exemplified for dummies👇🏻

Who Said Neural Networks Aren’t Linear?? In this paper, authors are able to collapse the training of diffusion models down to only 1 step by introducing Linearizer, which sandwiches a linear matrix A between two invertible neural networks!

askalphaxiv's tweet image. Who Said Neural Networks Aren’t Linear??

In this paper, authors are able to collapse the training of diffusion models down to only 1 step by introducing Linearizer, which sandwiches a linear matrix A between two invertible neural networks!

Hola🤠 ¿No sabes si usar #regresiónLineal, #logística o #multinivel? Te dejo esta mini guía para usar cada una en #R. 🌟Según el tipo de variable dependiente 🌟Según la distribución 🌟Con función para copiar y pegar 🫂El código acá ➡️ github.com/lesflores/regr… #CódigoDeTodxs

lesssflo's tweet image. Hola🤠 ¿No sabes si usar #regresiónLineal, #logística o #multinivel?

Te dejo esta mini guía para usar cada una en #R.

🌟Según el tipo de variable dependiente
🌟Según la distribución
🌟Con función para copiar y pegar

🫂El código acá ➡️ github.com/lesflores/regr…

#CódigoDeTodxs

Dimensionality Reduction is used across different ML domains and there are mainly two methods used to for this - > Linear Methods ( ex - PCA ) > Non-linear methods ( ex - t-SNE ) Linear methods assume that your data resides in a linear subspace ( Note - a linear subspace…

Pseudo_Sid26's tweet image. Dimensionality Reduction is used across different ML domains and there are mainly two methods used to for this -
> Linear Methods ( ex - PCA ) 
> Non-linear methods ( ex - t-SNE )  

Linear methods assume that your data resides in a linear subspace ( Note - a linear subspace…
Pseudo_Sid26's tweet image. Dimensionality Reduction is used across different ML domains and there are mainly two methods used to for this -
> Linear Methods ( ex - PCA ) 
> Non-linear methods ( ex - t-SNE )  

Linear methods assume that your data resides in a linear subspace ( Note - a linear subspace…
Pseudo_Sid26's tweet image. Dimensionality Reduction is used across different ML domains and there are mainly two methods used to for this -
> Linear Methods ( ex - PCA ) 
> Non-linear methods ( ex - t-SNE )  

Linear methods assume that your data resides in a linear subspace ( Note - a linear subspace…

Linear Regression clearly Explained! Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...) ❗️For the sake of simplicity we discuss linear regression with a single independent variable. Mathematical…

akshay_pachaar's tweet image. Linear Regression clearly Explained!

Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)

❗️For the sake of simplicity we discuss linear regression with a single independent variable.

Mathematical…
akshay_pachaar's tweet image. Linear Regression clearly Explained!

Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)

❗️For the sake of simplicity we discuss linear regression with a single independent variable.

Mathematical…
akshay_pachaar's tweet image. Linear Regression clearly Explained!

Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)

❗️For the sake of simplicity we discuss linear regression with a single independent variable.

Mathematical…

So this was the best project I built around Linear Regression while covering more types of regression in Machine Learning


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