AI 용어집
인공지능 완전 사전
Univariate Analysis
Individual examination of each variable to understand its distribution, central tendency, and dispersion.
Bivariate Analysis
Study of relationships and interactions between two variables to identify correlations and dependencies.
Multivariate Analysis
Simultaneous exploration of three or more variables to uncover complex patterns and interactions.
Data Visualization
Graphical representation of data using histograms, scatter plots, box plots, and heatmaps to reveal insights.
Anomaly Detection
Identification of outliers and unusual observations that may influence the analysis.
Distribution Analysis
Study of data distribution characteristics including normality, skewness, and kurtosis.
Correlation Analysis
Quantitative measurement of linear relationships between continuous variables using Pearson, Spearman, and Kendall.
Dimensionality Reduction
Techniques such as PCA and t-SNE to simplify data while preserving essential information.
Time Series Analysis
Exploration of chronologically ordered data to identify trends, seasonality, and cycles.
Categorical Data Analysis
Examination of qualitative variables using contingency tables and chi-square tests.
Missing Data Analysis
Identification and characterization of missing values to determine patterns and impacts.
Cluster Analysis
Natural identification of groupings in data using algorithms such as K-means and hierarchical clustering.
Extreme Value Analysis
Systematic study of extreme observations to understand their nature and impact on analysis.
Data Transformation
Application of mathematical transformations to normalize and stabilize data variance.
Robustness Analysis
Evaluation of the sensitivity of results to extreme values and underlying assumptions.