MLE Estimation
MAP Estimation
Regression Methods
Controls
Dataset Selection
Apartment Dataset
Housing Dataset
Regression Method
OLS
Ridge
Lasso
Elastic Net
λ (Lambda)
1.000
0.001
1
1000
λ₁ (L1 Weight)
0.50
λ₂ (L2 Weight)
0.50
Current λ:
1.000
Optimal λ:
--
Dataset:
12 samples, 3 features
Equation
Key Values
Matrices & Dimensions
Step-by-Step: X
T
X + λI
Covariance Matrices
Weights & Prediction
Weights Evolution with λ
Gaussian Distributions
Error Distribution
Prior Distribution
Posterior Distribution
Dataset Statistics
Feature-Target Correlation
Correlation Metrics Summary
λ Effect Narrative
Optimal λ Determination
Criterion
Value
Optimal λ
Status
CV Error vs λ
AIC vs λ
BIC vs λ
λ vs MSE / R²
λ vs Weight Magnitude
λ vs Non-Zero Weights
Predicted vs Actual
Correlation Matrices (Rxx)
Feature Correlation Matrix (Rxx)
Feature-Target Correlation (Rxy)
▼ Detailed Equation Values
MSE & R² vs Lambda
Weight Norms (L1 & L2)
Sparsity (Non-zero Weights)
Gradient Norm
Loss Components
MLE & MAP
▼ Parameter Correlations
λ vs Norms
λ vs Loss Components
Data Loss vs Reg Loss
▼ Detailed Equation Values
MSE & R² vs Lambda
Weight Norms (L1 & L2)
Sparsity (Non-zero Weights)
Gradient Norm
Loss Components
MLE & MAP
▼ Parameter Correlations
λ vs Norms
λ vs Loss Components
Data Loss vs Reg Loss