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Fix typos in Beyond Binary Classification #315

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Fix typos in Beyond Binary Classification #315

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6 changes: 2 additions & 4 deletions book/complex.tex
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Expand Up @@ -13,8 +13,6 @@ \chapter{Beyond Binary Classification} \label{sec:complex}
regression.
\end{learningobjectives}

\dependencies{}

\newthought{In the preceeding chapters,} you have learned all about a
very simple form of prediction: predicting bits. In the real world,
however, we often need to predict much more complex objects. You may
Expand Down Expand Up @@ -70,9 +68,9 @@ \section{Learning with Imbalanced Data} \label{sec:imbalanced}
that you are left with a balanced data set ($50\%$ positive, $50\%$
negative). This might scare you a bit since throwing out data seems
like a bad idea, but at least it makes learning much more efficient.
In weighting, instead of throwing out positive examples, we just give
In weighting, instead of throwing out negative examples, we just give
them lower weight. If you assign an \concept{importance weight} of
$0.00101$ to each of the positive examples, then there will be as much
$0.00101$ to each of the negative examples, then there will be as much
\emph{weight} associated with positive examples as negative examples.

Before formally defining these heuristics, we need to have a mechanism
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