Random Forest is a machine learning model used for making predictions. It uses a collection of decision trees, each trained on a random subset of data, to enhance prediction accuracy.
Decision trees are a simpler type of model that use a series of decisions to classify data. They are susceptible to problems like bias and overfitting.
Imagine deciding whether to play golf. A decision tree might consider factors like time, weather, and availability of clubs.
Overfitting occurs when a model memorizes the training data instead of generalizing from it. This makes the model less effective at predicting future data.
Bias in a decision tree can arise when the data is not split evenly during training, leading to an incomplete view of the data points.
Random Forest helps reduce overfitting and bias by combining predictions from multiple decision trees. If some trees are irrelevant, they are ignored.
Using multiple decision trees with different criteria improves the Random Forest's performance. Think of it like getting advice from several people who have built different decision trees based on their own experiences.
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