Housing Model Development

This repository began as a Udacity Nanodegree project working on the Boston Housing Data. Final work for this early work in machine learning can be viewed here.

Later, this was reworked with a deeper emphasis on linear models.

Boston Redux

Baseline Model

A baseline model was assessed against three models:

• linear regression with no regularization (ordinary least squares)
• linear regresison with $\ell_1$ regularization (LASSO)
• linear regression with $\ell_2$ regularization (Ridge Regression)

A simple grid search was performed over the regularized models to identify an optimal coefficient for the regularization.

The results of this were

alpha	model	test_score	train_score
NaN	linear regression	0.711009	0.743956
0.00001	lasso	0.711009	0.743956
0.01000	ridge	0.711016	0.743956

Standardized Model

A standardized model was assessed against the same three models:

• linear regression with no regularization (ordinary least squares)
• linear regresison with $\ell_1$ regularization (LASSO)
• linear regression with $\ell_2$ regularization (Ridge Regression)

A simple grid search was performed over the regularized models to identify an optimal coefficient for the regularization.

The results of this were

alpha	model	test_score	train_score
NaN	linear regression	0.711009	0.743956
0.00001	lasso	0.711215	0.743880
0.01000	ridge	0.711298	0.742562

Note that standardization has no effect on the non-regularized linear regression.

Skew Normal, Standardized Model

A skew-normal, standardized model was assessed against the same three models:

• linear regression with no regularization (ordinary least squares)
• linear regresison with $\ell_1$ regularization (LASSO)
• linear regression with $\ell_2$ regularization (Ridge Regression)

A simple grid search was performed over the regularized models to identify an optimal coefficient for the regularization.

The results of this were

alpha	model	test_score	train_score
NaN	linear regression	0.751304	0.778260
0.00001	lasso	0.751307	0.778260
0.01000	ridge	0.751436	0.778242

Note that skew-normalization boosts both train and test performance for all three models.