Linear Discriminant Analysis Implementation in Python & R
Linear Discriminant Analysis (LDA) is a classifier that creates a linear decision boundary by fitting class-conditional densities to the data…
Stepwise Feature Selection +example
Stepwise feature selection is a systematic approach to identifying the most relevant features for a predictive model by combining both…
Backward feature selection + example
Backward feature selection involves iteratively removing the least significant feature from a model based on adjusted R-squared. In this example, we are predicting nuts collected by squirrels, features like temperature and rainfall are chosen as significant predictors through this method. The process aims to finalize a model with the most influential features.
Forward feature selection: a step by step example
Forward feature selection starts with an empty model and adds features one by one. At each step, the feature that…
ElasticNet Regression: Method & Codes
ElasticNet regression is a regularized regression method that linearly combines both L1 and L2 penalties of the Lasso and Ridge…
Lasso & Ridge regression: method & codes
Ridge regression: Ridge adds the penalty, which is the sum of the squares of the coefficients, to the loss function…
Lasso Regression: methods & codes
Lasso (Least Absolute Shrinkage and Selection Operator): On the Penalty of Lasso: Implementation: In the following, we will: You can…
Simple linear regression using train-test split in Python & R
An example of performing simple linear regression using train-test split where the process is as follows, 1. Generate a synthetic…
How to Write a Proof in a paper
Reviewers are not required to read supplementary materials, but many do. Therefore, making your proof easy to read is important.…