Sachin Kumar Profile picture
May 15 โ€ข 12 tweets โ€ข 4 min read Twitter logo Read on Twitter
Day 62 of #100DayswithMachineLearning

Topic - Ridge Regression in ML ( Part 1 )

๐Ÿงต Image
Ridge Regression (RR) is regularization technique used in statistical modeling & ML to handle the problem of multicollinearity (high correlation) among predictor variables Image
It is an extension of linear regression ( LR) that adds a penalty term to the least squares objective function, resulting in a more stable and robust model.
In LR objective is find coefficient thatโฌ‡๏ธ sum of squared residual btwn predicted value & actual value of dependent variable. However when there r correlated predictor in dataset the estimated coefficient can become highly sensitive to small change in data leading to overfitting
RR addresses issue by introducing penalty term that shrinks coefficient estimates towards 0.

The penalty term is determined by tuning parameter called lambda (ฮป) which control amount of regularization applied. larger value of ฮป result in greater shrinkage of the coefficients
Mathematically RR modifies OLS objective function by adding penalty term based on the L2-norm (Euclidean norm) of the coefficient vector

The objective function can be written as:

Minimize: (sum of squared residuals) + ฮป * (sum of squared coefficients) Image
The addition of the penalty term encourages the model to find a balance between minimizing the residuals and minimizing the magnitude of the coefficients.

This helps to reduce the impact of multicollinearity and stabilize the model's performance.
RR can solve using optimization algorithm such GD or closed-form solution

The optimal value of regularization parameter ฮป is typically determined through techniques like CV where data divided into training & validation set to evaluate model performance for different values of ฮป
Ridge regression is a useful technique for managing multicollinearity and improving the generalization ability of a linear regression model

By controlling the complexity of the model, it helps to prevent overfitting and provides more reliable estimates of the coefficients Image
We got an idea that ridge regression is a linear regression with a penalty. Learned that no equation could find the best value of lambda.

vitalflux.com/ridge-regressiโ€ฆ
Ridge regression is a powerful tool used in statistics and machine learning. It can be intimidating to learn at first, but with a few examples, it becomes much more manageable

@github Notebook

github.com/sachinkumar160โ€ฆ
๐Ÿ”นIf this thread was helpful to you

1. Follow me @Sachintukumar
for daily content

2. Connect with me on Linkedin: linkedin.com/in/sachintukumโ€ฆ

3. RT tweet below to share it with your friend

โ€ข โ€ข โ€ข

Missing some Tweet in this thread? You can try to force a refresh
ใ€€

Keep Current with Sachin Kumar

Sachin Kumar Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @Sachintukumar

May 15
Are you completely new to SQL and do not know where to start?

Here is a simple concept roadmap for learning SQL as a complete beginner:

๐Ÿงต Image
1. ๐‹๐ž๐š๐ซ๐ง ๐ญ๐ก๐ž ๐›๐š๐ฌ๐ข๐œ๐ฌ:

- Primary Key vs Foreign Key
- Data Types
- Database diagrams
- Tables
- Records and Fields
- Naming standards for tables and fields
2. ๐‘๐ž๐ญ๐ซ๐ข๐ž๐ฏ๐ข๐ง๐  ๐ƒ๐š๐ญ๐š ๐ฐ๐ข๐ญ๐ก ๐’๐„๐‹๐„๐‚๐“:

- Learn how to write SELECT ๐˜ค๐˜ฐ๐˜ญ๐˜ถ๐˜ฎ๐˜ฏ(๐˜ด) FROM ๐˜ต๐˜ข๐˜ฃ๐˜ญ๐˜ฆ
- Combine with other keywords: WHERE, ORDER BY & LIMIT
- Learn how to use arithmetic operators in SELECT statement
- Retrieve unique values with DISTINCT keyword
Read 8 tweets
May 14
Day61 of #100DayswithMachineLearning

Topic - Bias Variance Trade-off in ML

๐Ÿงต Image
๐Ÿ”น If ML model is not accurate. it can make predictions error & these prediction errors are usually known as Bias & Variance

๐Ÿ”น In ML these errors will alway be present as there is always slight difference between model predictions & actual predictions
๐Ÿ”นThe main aim of ML/data science analysts is to reduce these errors in order to get more accurate result

๐Ÿ”นIn ML an error is measure of how accurately an algorithm can make predictions for the previously unknown dataset Image
Read 12 tweets
May 13
Day 60 of #100DayswithMachineLearning

Topic - Polynomial Regression in ML

๐Ÿงต Image
Polynomial regression is type of regression analysis where relationship between independent variable(s) and dependent variable is modeled as an nth-degree polynomial function.

It is an extension of simple linear regression which assumes linear relationship between the variable
In polynomial regression, the polynomial function takes the form:

y = ฮฒโ‚€ + ฮฒโ‚x + ฮฒโ‚‚xยฒ + ... + ฮฒโ‚™xโฟ
Read 12 tweets
May 13
๐Ÿ”ธCONCAT_WS() in SQL { Very Helpful }

A Thread ๐Ÿงต Image
CONCAT_WS() function in SQL is used to concatenate multiple strings into single string with specified separator between each string

"WS" stands for "with separator." This function is commonly used to construct strings contain multiple values such create comma-separated list
The syntax for CONCAT_WS() is as follows:

๐Ÿ”ธCONCAT_WS(separator, string1, string2, ..., stringN)
Read 6 tweets
May 12
Day 59 of #100DayswithMachinelearning

Topic - Mini-Batch Gradient Descent

A Thread ๐Ÿงต Image
Mini-batch gradient descent is a variation of the gradient descent optimization algorithm used in ML & DL

It is designed to address the limitations of two other variants: BGD and SGD Image
In BGD the entire training dataset is used to compute the gradient of the cost function for each iteration.

This approach guarantees convergence to the global minimum but can be computationally expensive, especially for large datasets
Read 12 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(