Overfitting occurs when a model is too complex and captures noise rather than the underlying pattern.
A decision tree is a graphical representation used to make decisions based on various conditions and outcomes.
Decision trees assist in optimizing inventory levels and supplier selection in supply chain management.
The Gini Index is a metric used to evaluate the impurity of a dataset; lower values indicate better splits.
Limitations include the risk of overfitting, instability with small changes in data, and potential bias towards features with more levels.
Decision trees serve as a powerful tool in finance and operations, providing a structured approach to decision-making and enhancing strategic planning and operational efficiency.
In human resources, decision trees can evaluate candidates based on various attributes to make hiring decisions.
In finance, decision trees are used for risk assessment, credit scoring, and portfolio management.
A decision tree is composed of nodes (representing decisions or outcomes), branches (indicating possible options or paths), and leaves (final outcomes or decisions).
Advantages include simplicity, visual representation of the decision-making process, and flexibility in handling both numerical and categorical data.
Entropy is a measure of uncertainty or impurity in the dataset; it is used to determine the best split at each node.
In marketing, decision trees can segment customers based on purchasing behavior to tailor marketing strategies.
