Linear Discriminant Analysis (LDA) is a classifier that creates a linear decision boundary by fitting class-conditional densities to the data and applying Bayes’ rule. The model assumes that each class follows a Gaussian distribution with… Linear Discriminant Analysis Implementation in Python & R
Stepwise feature selection is a systematic approach to identifying the most relevant features for a predictive model by combining both forward and backward selection techniques. The process begins with either an empty model. Then, we… Stepwise 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 starts with an empty model and adds features one by one. At each step, the feature that improves the model performance the most is added to the model. The process continues until… Forward feature selection: a step by step example
ElasticNet regression is a regularized regression method that linearly combines both L1 and L2 penalties of the Lasso and Ridge methods. This allows it to perform both feature selection (like Lasso) and maintain some of… ElasticNet Regression: Method & Codes
Motivation Now, recall that for LASSO Ridge Regression: Ridge regression: Ridge adds the penalty, which is the sum of the squares of the coefficients, to the loss function in linear regression. Ridge regression shrinks the… Ridge regression: method & R codes
The Lasso (Least Absolute Shrinkage and Selection Operator) is a regression technique that enhances prediction accuracy and interpretability by applying L1 regularization to shrink coefficients. Unlike traditional regression methods, Lasso forces some coefficients to become… Lasso Regression and LassoCV: methods & Python codes
An example of performing simple linear regression using train-test split where the process is as follows, 1. Generate a synthetic dataset: 2. Split the dataset: We use train_test_split to divide the data into training and… Simple linear regression using train-test split in Python & R
Reviewers are not required to read supplementary materials, but many do. Therefore, making your proof easy to read is important. General Guidelines: Example: Theorem 1: Let and be real numbers. If , then . The… How to Write a Proof in a paper
Dosage Optimization: Pharmacologists use derivatives to find the optimal drug dosage that maximizes therapeutic effects while minimizing side effects. The concentration of the drug in the bloodstream is modeled as a function of time, and… Example of using derivatives to find optimal drug dosage