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Fix typos <noupdate>

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Tian Cheng (Riccardo) Xia
2024-01-14 14:22:30 +01:00
parent e48a993ccc
commit 53f901bef2
1 changed files with 2 additions and 2 deletions
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src/machine-learning-and-data-mining/sections/_classification.tex
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@ -515,7 +515,7 @@ Possible solutions are:
\subsection{Complexity} \subsection{Complexity}
Given a dataset $\matr{X}$ of $N$ instances and $D$ attributes, Given a dataset $\matr{X}$ of $N$ instances and $D$ attributes,
each level of the tree requires to evaluate all the dataset and each level of the tree requires to evaluate the whole dataset and
each node requires to process all the attributes. each node requires to process all the attributes.
Assuming an average height of $O(\log N)$, Assuming an average height of $O(\log N)$,
the overall complexity for induction (parameters search) is $O(DN \log N)$. the overall complexity for induction (parameters search) is $O(DN \log N)$.
@ -585,7 +585,7 @@ This has complexity $O(h)$, with $h$ the height of the tree.
If the value $e_{ij}$ of the domain of a feature $E_i$ never appears in the dataset, If the value $e_{ij}$ of the domain of a feature $E_i$ never appears in the dataset,
its probability $\prob{e_{ij} \mid c}$ will be 0 for all classes. its probability $\prob{e_{ij} \mid c}$ will be 0 for all classes.
This nullifies all the probabilities that use this feature when This nullifies all the probabilities that use this feature when
computing the product chain during inference. computing the chain of products during inference.
Smoothing methods can be used to avoid this problem. Smoothing methods can be used to avoid this problem.
\begin{description} \begin{description}