Machine Learning Concepts

Ridge & Lasso Regression

Regression models are widely used in data science and machine learning for predicting numerical outcomes. However, these models can suffer from overfitting when the number of features is large or when multicollinearity exists among predictors. To address these issues, regularization techniques like Ridge and Lasso regression come to the rescue. Both methods add penalty terms to the traditional linear regression cost function to control the model's complexity, but they do so in slightly different ways. Click here to read more about this in my blog or click here to go straight to the GitHub repo

Unleashing the Power of Random Forest Algorithm

In the world of machine learning, decision trees have long been a popular choice for building predictive models due to their simplicity and interpretability. However, decision trees often suffer from overfitting and lack robustness when exposed to complex and noisy datasets. This is where the Random Forest algorithm emerges as a powerful and superior solution. To learn more about this click here or go straight to the GitHub repo of this project here