Keyphrases
Least Squares Estimation
66%
Regression Method
33%
Portfolio Optimization
33%
Weighted K-nearest Neighbor
33%
Feature Subset
33%
Neighborhood Rules
33%
Extended Neighborhood
33%
Subagging
33%
Causal Tree
33%
Microarray Gene Expression
33%
Optimal Model Selection
33%
Weighted Signal-to-noise Ratio
33%
Student Performance Data
33%
Prediction Factors
33%
Ridge Regression
27%
Portfolio Performance
27%
Mean Squared Prediction Error
22%
Decision Tree Forest
22%
Simulated Dataset
20%
Expected Returns
16%
Real-time Data Analysis
16%
Non-sparse
16%
Least Absolute Shrinkage and Selection Operator (LASSO)
16%
Art Features
16%
Simulation Study
16%
Unbiased Estimate
16%
Feature Selection Problem
16%
London Stock Exchange
16%
Regularization Method
16%
Specific Risk
16%
Biased Estimation
16%
Regression Problem
16%
Boxplot
16%
Graphical Models
16%
Linear Regression
16%
Study Habits
16%
Unusual Patterns
16%
Practical Decision
16%
Academic Failure
16%
Weighted Distance
13%
Ridge Method
11%
Unbiased Estimation
11%
Coefficient Estimates
11%
Causal Forest
11%
Regression Predict
11%
Out-of-sample Error
11%
Shrinkage Parameter
11%
Disjoint Groups
11%
Worker Age
11%
Ridge Regression Model
11%
Computer Science
Binary Classification
100%
Bootstrap Sample
83%
Ensemble Method
66%
Feature Space
33%
Predicted Class
33%
Majority Voting
33%
Classification Accuracy
33%
Performance Metric
33%
Simulation Study
33%
Feature Selection
33%
Classification Problem
33%
Ridge Regression
33%
Support Vector
33%
Random Projection
33%
Treatment Effect
33%
Gene Expression Data
33%
Random Decision Forest
33%
Online Learning
33%
Performance Data
33%
Ensemble Learning
33%
Selection Operator
16%
Support Vector Machine
16%
Nearest Neighbors Classifier
16%
Response Variable
16%
Prediction Error
16%
Simulated Data
16%
Prediction Performance
16%
Student Interaction
16%
Collected Data
16%
Machine Learning
16%
Lower Performance
16%
Internet Connectivity
16%
Decision-Making
16%
Error Estimate
11%
Dimensional Classification
5%
Discriminatory Power
5%
Mathematics
Ridge
66%
Least Square Estimation
66%
Ridge Regression
60%
Simulation Study
40%
Graphical Model
40%
Real Data
40%
Box Plot
38%
Regularization
33%
Noise Ratio
33%
Covariate
33%
Robust Measure
33%
Biased Estimate
33%
Linear Regression
33%
Class Label
33%
Test Point
33%
Model Selection
33%
Mean Square Error
33%
Binary Classification
33%
Sample Point
33%
Bagging
33%
Dimensional Data
23%
Sharpe Ratio
16%
Boxplots
16%
Bounded Region
16%
Random Subset
16%
Bootstrap Sample
16%
Feature Space
16%
Classical Method
16%
Causal Relationship
11%
Pearson Correlation
11%
Error Estimate
11%
Multicollinearity
6%
Apply It
6%
Desirable Property
6%
Simulated Data
5%
Execution Time
5%
Support Vector Machine
5%
Classification Problem
5%
Computational Cost
5%
