Classification Algorithm 편집하기 (부분)

==Types of Classification Algorithms==
There are various types of classification algorithms, each with unique approaches and strengths:
*'''[[Logistic Regression]]''': A simple yet powerful algorithm for binary classification. It predicts probabilities using a logistic function and classifies data based on a threshold, making it suitable for tasks like spam detection and credit scoring.
*'''[[K-Nearest Neighbor|K-Nearest Neighbor (K-NN)]]''': A non-parametric algorithm that classifies data based on the majority class among its nearest neighbors. It's easy to implement and intuitive but computationally intensive for large datasets.
*'''[[Support Vector Machine|Support Vector Machine (SVM)]]''': Finds the optimal boundary (hyperplane) that separates classes. SVM is effective for high-dimensional data and works well for binary classification tasks.
*'''[[Decision Tree]]''': Builds a tree structure where each node represents a feature, and branches represent decisions. Decision trees are easy to interpret and suitable for both binary and multiclass classification.
*'''Random Forest''': An ensemble of multiple decision trees that improves accuracy and reduces overfitting. Random forests are widely used in applications requiring high accuracy and robustness.
*'''[[Naive Bayes]]''': A probabilistic classifier based on Bayes' theorem, assuming independence among features. It's fast, simple, and effective for text classification tasks like spam detection.
*'''Neural Networks''': Use interconnected layers of nodes to capture complex patterns in data. Deep neural networks are particularly powerful in image and speech recognition tasks but require large datasets and computational power.
*'''Gradient Boosting (e.g., XGBoost, LightGBM)''': An ensemble technique that builds a series of weak learners, often decision trees, to improve accuracy. Gradient boosting is effective in handling complex datasets and is frequently used in machine learning competitions.
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