In this lesson, I explored the balance between underfitting and overfitting in decision trees and introduced the random forest model as a solution. Decision trees can either overfit with too much specificity or underfit with too little. Random forests address this by averaging predictions from multiple trees, providing better accuracy and robustness.
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In this lesson we explored the balance between underfitting and overfitting in decision trees and introduced the random forest model as a solution. Decision trees can either overfit with too much specificity or underfit with too little. Random forests address this by averaging predictions from multiple trees providing better accuracy and robustness
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In this lesson we explored the balance between underfitting and overfitting in decision trees and introduced the random forest model as a solution. Decision trees can either overfit with too much specificity or underfit with too little. Random forests address this by averaging predictions from multiple trees providing better accuracy and robustness
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