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DATA MINING ALGORITHMS - Coggle Diagram
DATA MINING ALGORITHMS
Algorithms
Classification trees
Splits data into subsets based on feature values.
Linear discriminant functions
Finds a linear boundary to separate classes.
Support vector machines (SVMs)
Maximizes margin between classes.
Margin Maximization
SVM aims to maximize the distance between classes."
Hinge Loss
Penalty for misclassification in SVMs
Logistic regression
Predicts binary outcomes using probabilities.
Probability Estimation
Estimates the probability of a class (0 or 1).
Log-Odds
Transforms probabilities into odds (logarithmic scale).
Objective Functions
Loss functions
Quantify error between predicted and actual values.
Mean Squared Error
Common loss function used in regression.
Cross-Entropy Loss
Used in classification tasks for probabilistic models.
Hinge loss
Used in SVMs to penalize misclassified points.
Margin maximization
Focuses on maximizing the margin in SVM for better classification.
Least squares error
Minimizes squared differences in regression models.
Key Components
Decision boundaries
Lines or surfaces that separate different classes.
Weight
Parameters that control the influence of each feature.
Feature Importance
Weights show which features are most important in prediction.
Error penalties
Discourage large errors and improve model generalization.
Regularization
A technique to prevent overfitting by adding a penalty term.
Model Types
Linear models
Assume a straight-line relationship between inputs and outputs.
Linear Regression
Predicts continuous values using a straight line.
Logistic Regression
Predicts probabilities for binary outcomes.
Nonlinear models
Capture complex relationships with curves or other functions.
Decision Trees
Split data based on feature values, forming a tree structure.
SVM with Kernels
Uses kernels to map data into higher dimensions for better separation.
Applications
Fraud detection
Identifies fraudulent transactions by analyzing data patterns.
Customer segmentation
Groups customers based on behavior for targeted marketing.
Other applications
Recommendation Systems
Suggest products based on user preferences and past behavior.
Predictive Maintenance
Predicts equipment failure to schedule maintenance.
Healthcare
Uses data mining for early disease detection and personalized treatment.