From e3b6980057553f2170db11e9830262147368ac39 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Felix=20Ramnel=C3=B6v?= <felra653@student.liu.se>
Date: Mon, 18 Nov 2024 13:15:06 +0100
Subject: [PATCH] Lab 1: Fixes
---
lab1/assignment2.R | 7 ++++---
lab1/lab-notes.md | 12 ++++++------
2 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/lab1/assignment2.R b/lab1/assignment2.R
index f196d67..6e760d4 100644
--- a/lab1/assignment2.R
+++ b/lab1/assignment2.R
@@ -54,14 +54,15 @@ print(paste("MSE on the test data:", mse(test_scaled$motor_UPDRS, test_pred)))
# Loglikelihood
log_likelihood <- function(theta, sigma) {
n <- length(y_train)
- ll <- -n / 2 * log(sqrt(2 * pi) * sigma) - (1 / (2 * sigma ^ 2)) * sum((y_train -
- as.matrix(X_train) %*% theta) ^ 2)
+ ll <- -n / 2 * log(2 * pi) - n * log(sigma) - (1 / (2 * sigma ^ 2)) * sum((y_train -
+ as.matrix(X_train) %*% theta) ^
+ 2)
return(ll)
}
# Ridge
ridge <- function(theta, sigma, lambda) {
- ridge <- - log_likelihood(theta, sigma) + lambda * sum(theta ^ 2)
+ ridge <- -log_likelihood(theta, sigma) + lambda * sum(theta ^ 2)
return(ridge)
}
diff --git a/lab1/lab-notes.md b/lab1/lab-notes.md
index 8229c4a..e2f8342 100644
--- a/lab1/lab-notes.md
+++ b/lab1/lab-notes.md
@@ -112,16 +112,16 @@ Confusion matrix o misclassification error e framtana.
4. Optimal $\bold{\theta}$ for $\lambda \in \{1,100,1000\}$:
- $\lambda = 1$:
- - $\text{MSE}_{\text{train}} = 0.878681448897974$:
- - $\text{MSE}_{\text{test}} = 0.934684486872397$:
+ - $\text{MSE}_{\text{train}} = 0.878627075979604$:
+ - $\text{MSE}_{\text{test}} = 0.93499698041696$:
- $df = 13.8607362829965$
- $\lambda = 100$:
- - $\text{MSE}_{\text{train}} = 0.889775499501371$
- - $\text{MSE}_{\text{test}} = 0.934131808081541$
+ - $\text{MSE}_{\text{train}} = 0.884410431119223$
+ - $\text{MSE}_{\text{test}} = 0.932331719427938$
- $df = 9.92488712829542$
- $\lambda = 1000$:
- - $\text{MSE}_{\text{train}} = 0.939949118364897$
- - $\text{MSE}_{\text{test}} = 0.967756869359676$
+ - $\text{MSE}_{\text{train}} = 0.921107873074395$
+ - $\text{MSE}_{\text{test}} = 0.953955470516858$
- $df = 5.6439254878463$
$\lambda = 100$ seems to be the most suitable penalty parameter considering
--
GitLab