Backwared Elimination in Linear Regression Model Building

Backward Elimination

  1. Select a significance level to stay in the model (eg. SL = 0.05)
  2. Fit the full model with all possible predictors
  3. Consider the predictor with the highest P-value. If P > SL, go to STEP 4, otherwise go to FIN
  4. Remove the predictor
  5. Fit model without this variable
Back to (3)

FIN - you're done. Congratulations - you've applied LR to build an ML model!

If you're using Scikit-Learn, the module automatically selects the statistically significant features, but, if you want to see how BE is done, check out HdP's videos on DropBox

Comments

Post a Comment

Popular posts from this blog

The Dummy Variable Trap

Image
Watching this lecture, I felt like I was seeing a case of the right hand not knowing what the left hand was doing since Hadelin de Ponteves has pointed out the case of needing to transform a "name" or "state" feature using one-hot-encoding with ColumnTransformer. KE is doing the same thing and calling it creation of dummy variables. Cool stuff - always drop one of the "dummy variables" you generate using one-hot-encoding. chatG: Tools like pandas.get_dummies and OneHotEncoder (with drop='first' ) in sklearn can automatically exclude one dummy variable:
Read more

Your Handy ML Reference

Interactive Blog Table Interactive Blog Post Support Vector Machines for Classification Classifying data points Applications: Image recognition Click on a heading or short description to see more details here. Support Vector Regression Predicting continuous values Applications: Stock price forecasting Hidden Markov Model Modeling sequences Applications: Speech recognition Bioinspired Computing: Swarm Intelligence Distributed optimization Applications: Robotics Deep Neural Networks Multi-layer perceptron Applications: Language translation Cortical Algorithms ...
Read more

Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers

Image
Wow - giving it away for free in the hope that it helps build a better world:  https://core.ac.uk/reader/334586725 Machine Learning Machine Learning and Knowledge Discovery Support Vector Machines for Classification Support Vector Regression Hidden Markov Model Bioinspired Computing: Swarm Intelligence Deep Neural Networks Cortical Algorithms Deep Learning Multiobjective Optimization Machine Learning in Action: Examples
Read more