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Fix typos in Decision Trees #313

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Fix typos in Decision Trees #313

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e5453ed
Fix typos in Decision Trees
bharatr21 May 30, 2020
a84c45c
Fix more typos in perc.tex
bharatr21 May 31, 2020
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10 changes: 5 additions & 5 deletions book/dt.tex
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Expand Up @@ -247,7 +247,7 @@ \section{The Decision Tree Model of Learning}
like this course.

More formally, you will consider each feature in turn. You might
consider the feature ``Is this a System's course?'' This feature has
consider the feature ``Is this a Systems course?'' This feature has
two possible value: no and yes. Some of the training examples have an
answer of ``no'' -- let's call that the ``NO'' set. Some of the
training examples have an answer of ``yes'' -- let's call that the
Expand All @@ -269,16 +269,16 @@ \section{The Decision Tree Model of Learning}
In the YES set (where you guessed ``hate'') you would classify $8$
(out of $10$) of them correctly. So overall you would classify $18$
(out of $20$) correctly. Thus, we'll say that the \emph{score} of the
``Is this a System's course?'' question is $18/20$.
``Is this a Systems course?'' question is $18/20$.

\thinkaboutit{How many training examples would you classify correctly
for each of the other three features from
Figure~\ref{fig:dt_histogram}?}

You will then repeat this computation for each of the available
features to us, compute the scores for each of them. When you must
choose which feature consider first, you will want to choose the one
with the highest score.
features to us and compute the scores for each of them. When you must
choose which feature you should consider first, you will want to choose
the one with the highest score.

But this only lets you choose the \emph{first} feature to ask about.
This is the feature that goes at the \emph{root} of the decision tree.
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4 changes: 2 additions & 2 deletions book/perc.tex
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Expand Up @@ -79,7 +79,7 @@ \section{Bio-inspired Learning}
example. Otherwise it predicts a negative example.

The weights of this neuron are fairly easy to interpret. Suppose that
a feature, for instance ``is this a System's class?'' gets a zero
a feature, for instance ``is this a Systems class?'' gets a zero
weight. Then the activation is the same regardless of the value of
this feature. So features with zero weight are ignored. Features
with positive weights are indicative of positive examples because they
Expand All @@ -88,7 +88,7 @@ \section{Bio-inspired Learning}
decrease.

\thinkaboutit{What would happen if we encoded binary features like
``is this a System's class'' as no=$0$ and yes=$-1$ (rather than the
``is this a Systems class'' as no=$0$ and yes=$-1$ (rather than the
standard no=$0$ and yes=$+1$)?}

It is often convenient to have a non-zero \concept{threshold}. In
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