Classification Algorithm 편집하기 (부분)

==Choosing a Classification Algorithm==
The choice of a classification algorithm depends on factors like dataset size, feature dimensionality, interpretability, and computational constraints. For example:
*'''Logistic Regression''': Ideal for simple, binary classification problems with a linear decision boundary.
*'''K-NN or Decision Trees''': Useful for interpretable models, though K-NN is computationally intensive.
*'''SVM''': Suitable for high-dimensional data with a clear margin of separation.
*'''Ensemble Methods (Random Forest, Gradient Boosting)''': Provide high accuracy, especially on complex datasets, at the cost of interpretability and computational efficiency.
Experimenting with multiple algorithms and using cross-validation for evaluation can help determine the most suitable model for a particular classification problem.
[[Category:Data Science]]