차원 축소 및 특징 추출
특징 변환 기법은 데이터를 새 특징으로 변환하여 데이터의 차원 수를 줄입니다. 데이터에 categorical형 변수가 있는 경우와 같이 변수 변환이 가능하지 않은 경우 특징 선택 기법이 더 적합합니다. 특정적으로 최소제곱 피팅에 적합한 특징 선택 기법에 대한 자세한 내용은 단계적 회귀 항목을 참조하십시오.
|Univariate feature ranking for classification using chi-square tests|
|Rank features for classification using minimum redundancy maximum relevance (MRMR) algorithm|
|Feature selection using neighborhood component analysis for classification|
|Univariate feature ranking for regression using F-tests|
|Feature selection using neighborhood component analysis for regression|
|Rank features for unsupervised learning using Laplacian scores|
|Compute partial dependence|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees|
|Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees|
|Estimates of predictor importance for classification tree|
|Estimates of predictor importance for classification ensemble of decision trees|
|Estimates of predictor importance for regression tree|
|Estimates of predictor importance for regression ensemble|
|Rank importance of predictors using ReliefF or RReliefF algorithm|
|Sequential feature selection using custom criterion|
|Perform stepwise regression|
|Create generalized linear regression model by stepwise regression|
t-SNE 다차원 시각화
PCA와 정준 상관
음이 아닌 행렬 분해
Learn about feature selection algorithms and explore the functions available for feature selection.
This topic introduces to sequential feature selection and provides an example that selects features sequentially using a custom criterion and the
Neighborhood component analysis (NCA) is a non-parametric method for selecting features with the goal of maximizing prediction accuracy of regression and classification algorithms.
- Robust Feature Selection Using NCA for Regression
- Tune Regularization Parameter to Detect Features Using NCA for Classification
Make a more robust and simpler model by removing predictors without compromising the predictive power of the model.
Select split-predictors for random forests using interaction test algorithm.
Feature extraction is a set of methods to extract high-level features from data.
This example shows a complete workflow for feature extraction from image data.
This example shows how to use
rica to disentangle mixed audio signals.
t-SNE 다차원 시각화
t-SNE is a method for visualizing high-dimensional data by nonlinear reduction to two or three dimensions, while preserving some features of the original data.
This example shows how t-SNE creates a useful low-dimensional embedding of high-dimensional data.
This example shows the effects of various
Output function description and example for t-SNE.
Factor analysis is a way to fit a model to multivariate data to estimate interdependence of measured variables on a smaller number of unobserved (latent) factors.
Use factor analysis to investigate whether companies within the same sector experience similar week-to-week changes in stock prices.
This example shows how to perform factor analysis using Statistics and Machine Learning Toolbox™.
음이 아닌 행렬 분해
Nonnegative matrix factorization (NMF) is a dimension-reduction technique based on a low-rank approximation of the feature space.
Perform nonnegative matrix factorization using the multiplicative and alternating least-squares algorithms.
Multidimensional scaling allows you to visualize how near points are to each other for many kinds of distance or dissimilarity metrics and can produce a representation of data in a small number of dimensions.
cmdscale to perform classical (metric) multidimensional scaling, also known as principal coordinates analysis.
This example shows how to perform classical multidimensional scaling using the
cmdscale function in Statistics and Machine Learning Toolbox™.
This example shows how to visualize dissimilarity data using nonclassical forms of multidimensional scaling (MDS).
Perform nonclassical multidimensional scaling using
Procrustes analysis minimizes the differences in location between compared landmark data using the best shape-preserving Euclidean transformations.
Use Procrustes analysis to compare two handwritten numerals.