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Marco Realacci 2025-01-18 00:20:19 +01:00
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8
.obsidian/workspace.json vendored
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@ -13,12 +13,12 @@
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"file": "Foundation of data science/notes/9 Gradient Boosting.md",
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"title": "9 Gradient Boosting"
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@ -254,7 +254,9 @@
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@ -286,8 +288,6 @@
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4
Foundation of data science/notes/9 Random Forest.md
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@ -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 $\log n$.