Digital Human Experiment
Big Data - Regression & Classification algorithms
Regression Analysis of Boston Housing Data Google Colab Link In my exploration of regression algorithms using the Boston Housing dataset, I tried several models to predict housing prices. Gradient Boost Tree Regressor t his model stands out due to its impressive performance metrics. It achieves a Root Mean Square Error (RMSE) of 3.67409 on the test data, coupled with an R-squared value of 0.84279, indicating a strong fit compared to the basic linear regression models. Random Forest Regressor This model displays robustness similar to the Gradient Boost Tree, with a comparable RMSE and an R-squared value of 0.786508. It offers a reliable alternative with its ensemble approach. Decision Tree Regressor Although slightly less effective than the Random Forest, the Decision Tree Regressor still shows substantial predictive capability with an R-squared value of 0.727668, illustrating its utility in scenarios where model interpretability is key. Classification for SMS Spam Detection Google Cola...
Comments
Post a Comment