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Gradient boosting improves model accuracy by sequentially adding weak learners (usually decision trees) where each new model corrects the errors of the previous one. It minimizes the loss function through gradient descent, allowing the ensemble to become more accurate with each iteration. It’s especially powerful for structured/tabular data.
Also, if you’re building data science dashboards or visual reports and want to stylize section headers or labels, you can visit site to generate bold or fancy text—adds a nice visual touch to presentations or notebooks.