Project 11: Hyperparameter tuning of XGBoost | Exploratory Data Analysis | Heart disease dataset.
Hyperparameter tuning of XGBoost involves systematically selecting the best set of hyperparameters, such as learning rate and tree depth, through techniques like grid search or random search, to optimize the XGBoost algorithm's performance and enhance its predictive accuracy on a given dataset, like the heart disease dataset. Exploratory Data Analysis (EDA) for the heart disease dataset involves analyzing and visualizing its features to uncover patterns, relationships, and potential outliers, providing valuable insights for understanding the data's characteristics and informing subsequent modeling decisions.
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