From b79e6352be12a43d9a420d3542d87fde9cf747d0 Mon Sep 17 00:00:00 2001
From: Marco Realacci <marco@marcorealacci.me>
Date: Sat, 18 Jan 2025 00:20:19 +0100
Subject: [PATCH] vault backup: 2025-01-18 00:20:19

---
 .obsidian/workspace.json                            | 8 ++++----
 Foundation of data science/notes/9 Random Forest.md | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json
index 24c83b1..52fe9ab 100644
--- a/.obsidian/workspace.json
+++ b/.obsidian/workspace.json
@@ -13,12 +13,12 @@
             "state": {
               "type": "markdown",
               "state": {
-                "file": "Foundation of data science/notes/9 Gradient Boosting.md",
+                "file": "Foundation of data science/notes/9 Decision tree.md",
                 "mode": "source",
                 "source": false
               },
               "icon": "lucide-file",
-              "title": "9 Gradient Boosting"
+              "title": "9 Decision tree"
             }
           },
           {
@@ -254,7 +254,9 @@
   },
   "active": "1fb39a1dfc7b5200",
   "lastOpenFiles": [
+    "Foundation of data science/notes/9 K-Nearest Neighbors.md",
     "Foundation of data science/notes/9 XGBoost.md",
+    "Foundation of data science/notes/9 Random Forest.md",
     "Foundation of data science/notes/9 Gradient Boosting.md",
     "Biometric Systems/notes/8 Face anti spoofing.md",
     "Biometric Systems/notes/3. Recognition Reliability.md",
@@ -286,8 +288,6 @@
     "Foundation of data science/notes/1 CV Basics.md",
     "Foundation of data science/slides/More on Neural Networks (1).pdf",
     "Foundation of data science/slides/Normal_equation_poly_lwr.pdf",
-    "Foundation of data science/notes/9 Random Forest.md",
-    "Foundation of data science/notes/9 K-Nearest Neighbors.md",
     "Foundation of data science/slides/binary_classification.pdf",
     "Biometric Systems/images/Pasted image 20241228171617.png",
     "Biometric Systems/images/Pasted image 20241228174722.png",
diff --git a/Foundation of data science/notes/9 Random Forest.md b/Foundation of data science/notes/9 Random Forest.md
index f942d0e..e6674ff 100644
--- a/Foundation of data science/notes/9 Random Forest.md	
+++ b/Foundation of data science/notes/9 Random Forest.md	
@@ -73,8 +73,8 @@ L'algoritmo costruisce molti alberi decisionali su sottoinsiemi casuali del data
 ### **Complessità Computazionale**
 
 - **Training:**  
-    Per un singolo albero: $O(d \cdot n \log n)$, dove dd è il numero di feature e nn il numero di campioni.  
-    Con TT alberi: $O(T \cdot d \cdot n \log n)$.
+    Per un singolo albero: $O(d \cdot n \log n)$, dove d è il numero di feature e n il numero di campioni.  
+    Con T alberi: $O(T \cdot d \cdot n \log n)$.
     
 - **Predizione:**  
     Predire su un campione richiede $O(T \cdot \text{depth})$, dove la profondità ($\text{depth}$) è proporzionale a $\log n$.