Machine Learning Methods

Explore three powerful ML techniques: LASSO for feature selection, Decision Trees for interpretability, and Neural Networks for complex patterns

Controls

Data Pattern

Number of Features: 10

Regularization (λ): 0.10

Higher λ = more aggressive feature selection

Observations:200

Features:10

Pattern:linear

R² = 0.634

X1:

2.961

X2:

1.671

X3:

1.136

X4:

0.007

X5:

-0.406

X6:

0.105

X7:

0.000

X8:

0.000

X9:

0.000

X10:

-0.115

Selected Features: 7 / 10

Try adjusting λ for better selection

Method	Best For	Interpretability	R²
LASSO	Feature selection, sparse models	⭐⭐⭐ High	0.634
Decision Tree	Nonlinear patterns, interactions	⭐⭐⭐ High	0.815
Neural Network	Complex patterns, large data	⭐ Low	0.985

You've explored three foundational ML methods. Now dive deeper into causal inference and advanced econometric techniques.