K-Means Clustering Method & Python Codes
Kurious Fox K-Means Clustering is a popular unsupervised machine learning algorithm used for clustering data into groups. It is widely…
machine learning in a random forest
Logistic regression with L1 or L2 penalty with codes in Python and R
Logistic regression with L1 or L2 penalty adds regularization to prevent overfitting and improve model generalization. L1 penalty…
What’s classification
Classification organizes items based on criteria. In data, it involves sorting into categories. It's manual or automated with…
Adjusted R squared
The coefficient of determination, or R-squared, measures how well an independent variable explains the variability of a dependent…
Feature selection & Model Selection
Feature selection involves identifying and including essential variables in the model, possibly leading to improved performance and interpretability.…
Sum of Squares & coefficients of determination with Python & R codes
The coefficient of determination (R-squared) measures how well a model explains the variance of the response variable. In…
Non-constant variance in linear regression: a duck’s mood swing problem
This content provides an example of simulating and detecting heteroscedasticity in data using Python. We simulate the data,…
Multiple linear regression
Multiple linear regression is a powerful tool for modeling relationships between multiple independent variables and a single dependent…
Review: Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a statistical method that estimates parameters by maximizing the likelihood function. For example,…
Comparing forward, backward, stepwise feature selection
Forward selection adds features one by one, optimizing model performance but potentially missing the best subset. Backward selection…