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Morten Hjorth-Jensen
2021-11-14 16:39:11 +01:00
parent c64a22ebf0
commit 6e6a5c152c
9 changed files with 43 additions and 43 deletions
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doc/Projects/2021/Project3/html/._Project3-bs000.html
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@@ -359,7 +359,7 @@ cons of the various methods. Are there some methods which provide both
low variance and low bias?
</p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree. </p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree. </p>
<h2 id="introduction-to-numerical-projects" class="anchor">Introduction to numerical projects </h2>
<p>Here follows a brief recipe and recommendation on how to write a report for each
doc/Projects/2021/Project3/html/Project3-bs.html
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@@ -359,7 +359,7 @@ cons of the various methods. Are there some methods which provide both
low variance and low bias?
</p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree. </p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree. </p>
<h2 id="introduction-to-numerical-projects" class="anchor">Introduction to numerical projects </h2>
<p>Here follows a brief recipe and recommendation on how to write a report for each
doc/Projects/2021/Project3/html/Project3.html
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@@ -390,7 +390,7 @@ cons of the various methods. Are there some methods which provide both
low variance and low bias?
</p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree. </p>
<p><b>Hint</b>: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree. </p>
<h2 id="introduction-to-numerical-projects">Introduction to numerical projects </h2>
<p>Here follows a brief recipe and recommendation on how to write a report for each
doc/Projects/2021/Project3/ipynb/Project3.ipynb
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"cons of the various methods. Are there some methods which provide both\n",
"low variance and low bias?\n",
"\n",
"**Hint**: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree."
"**Hint**: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree."
]
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doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz
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@@ -329,7 +329,7 @@ of your model. Comment and discuss the results. Discuss the pros and
cons of the various methods. Are there some methods which provide both
low variance and low bias?
\textbf{Hint}: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree.
\textbf{Hint}: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree.
\subsection{Introduction to numerical projects}
doc/Projects/2021/Project3/pdf/Project3.pdf
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doc/Projects/2021/Project3/pdf/Project3.tex
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@@ -303,7 +303,7 @@ of your model. Comment and discuss the results. Discuss the pros and
cons of the various methods. Are there some methods which provide both
low variance and low bias?
\textbf{Hint}: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree.
\textbf{Hint}: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree.
\subsection*{Introduction to numerical projects}
doc/src/Projects/2021/Project3/Project3.do.txt
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@@ -196,7 +196,7 @@ of your model. Comment and discuss the results. Discuss the pros and
cons of the various methods. Are there some methods which provide both
low variance and low bias?
_Hint_: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For for example decision trees, this is represented by the depth of the tree.
_Hint_: when you use different methods, pay attention to how you represent (and understand) the complexity of the model. For example, when using decision trees you may represent the complexity of your model by the depth of the tree.