Enhancing Regression Models with Polynomial Features and L1 Lasso Regularization
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable…
Category: regression
Understanding Polynomial Regression: A Comprehensive Comic Guide with codes
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable…
Understanding Random Forests for Quantile Prediction with sklearn implementation
Random forests enhance predictive performance by allowing quantile predictions, offering insights into outcome variability. This method is vital for risk assessment, aiding informed decision-making in uncertain environments.
Simple Linear Regression Review: Sunlight & Selfie
Simple linear regression is a statistical method used to model and analyze the relationship between two continuous variables. Specifically, it…
AIC and BIC for Feature Selection
Akaike Information Criterion (AIC) Bayesian Information Criterion (BIC) Comparison and Use in Feature Selection By applying AIC and BIC in…
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…