From 6e6a5c152c3f30dc18b12384198cdca878013a84 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sun, 14 Nov 2021 16:39:11 +0100 Subject: [PATCH] minor update --- .../2021/Project3/html/._Project3-bs000.html | 2 +- .../2021/Project3/html/Project3-bs.html | 2 +- doc/Projects/2021/Project3/html/Project3.html | 2 +- .../2021/Project3/ipynb/Project3.ipynb | 74 +++++++++--------- .../Project3/ipynb/ipynb-Project3-src.tar.gz | Bin 194 -> 194 bytes doc/Projects/2021/Project3/pdf/Project3.p.tex | 2 +- doc/Projects/2021/Project3/pdf/Project3.pdf | Bin 234551 -> 234572 bytes doc/Projects/2021/Project3/pdf/Project3.tex | 2 +- .../Projects/2021/Project3/Project3.do.txt | 2 +- 9 files changed, 43 insertions(+), 43 deletions(-) diff --git a/doc/Projects/2021/Project3/html/._Project3-bs000.html b/doc/Projects/2021/Project3/html/._Project3-bs000.html index 1144eb817..dccf24e20 100644 --- a/doc/Projects/2021/Project3/html/._Project3-bs000.html +++ b/doc/Projects/2021/Project3/html/._Project3-bs000.html @@ -359,7 +359,7 @@ 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.

Introduction to numerical projects

Here follows a brief recipe and recommendation on how to write a report for each diff --git a/doc/Projects/2021/Project3/html/Project3-bs.html b/doc/Projects/2021/Project3/html/Project3-bs.html index 1144eb817..dccf24e20 100644 --- a/doc/Projects/2021/Project3/html/Project3-bs.html +++ b/doc/Projects/2021/Project3/html/Project3-bs.html @@ -359,7 +359,7 @@ 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.

Introduction to numerical projects

Here follows a brief recipe and recommendation on how to write a report for each diff --git a/doc/Projects/2021/Project3/html/Project3.html b/doc/Projects/2021/Project3/html/Project3.html index 2e06fadd8..5fe0f289b 100644 --- a/doc/Projects/2021/Project3/html/Project3.html +++ b/doc/Projects/2021/Project3/html/Project3.html @@ -390,7 +390,7 @@ 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.

Introduction to numerical projects

Here follows a brief recipe and recommendation on how to write a report for each diff --git a/doc/Projects/2021/Project3/ipynb/Project3.ipynb b/doc/Projects/2021/Project3/ipynb/Project3.ipynb index 7d98fd7d5..3fb613d09 100644 --- a/doc/Projects/2021/Project3/ipynb/Project3.ipynb +++ b/doc/Projects/2021/Project3/ipynb/Project3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "26d52fd9", + "id": "e6c08da8", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "236f6cfd", + "id": "0309e266", "metadata": { "editable": true }, @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "1d3c512a", + "id": "cedebbe4", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "4f7124c2", + "id": "9038e49a", "metadata": { "editable": true }, @@ -79,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "ae4474c6", + "id": "89190c80", "metadata": { "editable": true }, @@ -91,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "657d3638", + "id": "b2944795", "metadata": { "editable": true }, @@ -103,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "cd7e6b5c", + "id": "5d7f5f66", "metadata": { "editable": true }, @@ -115,7 +115,7 @@ }, { "cell_type": "markdown", - "id": "05c0260c", + "id": "55083a27", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "9108ddbb", + "id": "26bb3996", "metadata": { "editable": true }, @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "4e2b05cf", + "id": "99cbbdf4", "metadata": { "editable": true }, @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "e45fc3b7", + "id": "1cf8305f", "metadata": { "editable": true }, @@ -171,7 +171,7 @@ }, { "cell_type": "markdown", - "id": "f036e12d", + "id": "c76618e8", "metadata": { "editable": true }, @@ -185,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "52660f41", + "id": "ac2c5732", "metadata": { "editable": true }, @@ -197,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "c1fe35cf", + "id": "ead9d520", "metadata": { "editable": true }, @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "a1395ad8", + "id": "8db95bf3", "metadata": { "editable": true }, @@ -219,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "aa6df14a", + "id": "2f93712e", "metadata": { "editable": true }, @@ -229,7 +229,7 @@ }, { "cell_type": "markdown", - "id": "09d87b1d", + "id": "62a0980a", "metadata": { "editable": true }, @@ -241,7 +241,7 @@ }, { "cell_type": "markdown", - "id": "1dc28a4b", + "id": "28f54a78", "metadata": { "editable": true }, @@ -252,7 +252,7 @@ }, { "cell_type": "markdown", - "id": "9e6804ed", + "id": "e0068e8b", "metadata": { "editable": true }, @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "004eed40", + "id": "f9ae2916", "metadata": { "editable": true }, @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "877b7ed0", + "id": "88e71606", "metadata": { "editable": true }, @@ -286,7 +286,7 @@ }, { "cell_type": "markdown", - "id": "cb45cb03", + "id": "69bc12e7", "metadata": { "editable": true }, @@ -299,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "fe999d48", + "id": "fa2123d5", "metadata": { "editable": true }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "f6ff7ece", + "id": "27474b17", "metadata": { "editable": true }, @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "40735cb1", + "id": "1c4da408", "metadata": { "editable": true }, @@ -333,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "1464be77", + "id": "06021617", "metadata": { "editable": true }, @@ -343,7 +343,7 @@ }, { "cell_type": "markdown", - "id": "ed9d7c03", + "id": "b845d906", "metadata": { "editable": true }, @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "588fde58", + "id": "6c3956bc", "metadata": { "editable": true }, @@ -366,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "4fe15315", + "id": "15d51aa1", "metadata": { "editable": true }, @@ -382,7 +382,7 @@ }, { "cell_type": "markdown", - "id": "7fcf0aab", + "id": "356b0ce1", "metadata": { "editable": true }, @@ -399,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "3f519e6a", + "id": "5254f0ec", "metadata": { "editable": true }, @@ -415,7 +415,7 @@ }, { "cell_type": "markdown", - "id": "f1037588", + "id": "fb586020", "metadata": { "editable": true }, @@ -427,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "3a26e170", + "id": "8f43137a", "metadata": { "editable": true }, @@ -457,12 +457,12 @@ "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." ] }, { "cell_type": "markdown", - "id": "4c14d063", + "id": "5efcf1de", "metadata": { "editable": true }, @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "f565fe9e", + "id": "e53cd89a", "metadata": { "editable": true }, @@ -515,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "a762ce82", + "id": "f1b49648", "metadata": { "editable": true }, diff --git a/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz b/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz index 857624d163b7acd67e3fa759b84023922d63d2f1..c9332ae714d7c1812cea0dd320136e2463097e64 100644 GIT binary patch delta 151 zcmV;I0BHZh0m1 delta 151 zcmV;I0BHZh0m1C*_o<7L`j~8xt^b^vfba=B8k4lY22Tt1}QMsNY#=5 zf9{8>8U5S+U8>`#ggxC8FPWG(9{a&F<+Q(pWBaDV)2ES_r+)vp`#*so7vfl>lA>U7 zzQo3oASO?vvBCpk&$|za+d1X3Bom$V_7tmkSz=Y;zx#?1Vv! zZ61blXuKTPHecy8j5~9Ny9>qA!l~HEeSNN)ug!G#$(KWS1G5>}01k_H_cbuTy>WV` zVla|0-0ximC>;9%JRp73Z;&895}MHhm$4|3Z=Uv*IPq`m%W(>*((cmwS*mm*)}Ic8$Ab}^kNnF5f56~aHXV<^ zSH>+<0q*$J4-LR~IvhtPJ)y`P=|(avYO)LwY)$;d6Jh~5*oPP%YH286ZV9j&zs)yDed4iD~ zBkDAPZ84OGAl2iE>`r|6fAE%fJbK^CNDLaj=+F#c7>+432j**+uv#SmF;WDK6Sf;m z0s}a!wghoes;Zn=Yz_t}wz@J0a>2>UbwKu>Zq#Sa*OrAvq)ocLgl_8olHACH(YYD$)`-LTG;e~HB~R=C8Y2>#bYA?)WNBDihfKLPhc(JH73Y|R_PHr9m*sU9_s*Dx;ubGYU-ikAf(_FvskZuLR^Qq||A~NyUGpy!hzF$6l zek-d@{UnWB; z=dhh&utFb!6Qach(>R~FI_ih%%J~r_HggIjXQ??_R8vSw$CBL<@Bng-uO=r8)oGfD z>Yo`=iIsek7`Nn;jPnT`hAD+uHYo+?V4#45S3A*O0^&WftnTfvI352QjOmGzs%^WUj}#t3tS3 z_ewR2UI=jstFZ5oI^evQ*T0cLoenxUaHbaXL7GH=qzFdA`esyzx=3YO?u79GH(F!f zq-@A@5hlSfIkyv6#5Ve&xy1tkUr2Hu)^DAVSnMQlf14;=cRvfq)+gjU?BM|F z^BF?!?{C$WDvq;DH;A3eV`ha(SJ9_OCa58gm z1T7$@e`Mo_00Sa!&GeQ@r*eUp2EUqwUy!LK+iOg_LGg=E4*moH*~0D2hd6c{m#aKo zLpK{V27xO*FgBvGUU+hDA7WnofVJEDxD{uy zXFhINKWv6tUVYf?01#{Lel0MZro;B2#YZiPs|2s-j|!JcriC(vV#VO{}zO5cKcKcTljs3ybulvN34mG=pkEN87?VQY%Qzc z%QXUp_P8K}&?PLdJnuee6K_qED4pjJ^_;@W8-w1LM8_9EUo zmZw4XEK^CmeX^r(LNz||w;`2p1k+L1ER{0p)-yY2A0V7YKi8bkD@p4RVoAmr0}3%6 z%Ps^l{6slYEVcUFB>d85wO};Z^d@*x3r*$nf)H}|#@qRXbyClDI-je`5z83gN$!@$wqRQ>tTKvB?rhC0*ct%A>ptY%9Un z_)&LUg2{=Js2|!FOr5_Grj`mPRnCGwI#ZdIGWp+dplcLv5BMm_Yt#I4^2UTVgv8e~ zVSy|@OW^C(1jV0}i0zC~ZIrFtz1Zk$$B|#2E9i+vGI`SlR&LdeS)M#5e?LxF$g@yL zC$UvokuB}?CUI}zWtXb$%!9f3SQGX74C{Xzh!z@?{}zcFYMSt#y8scg@tGzrv;jQv zO?WSNVlhY8@HBG(qK@jw^nS~S?Hf<060Vdw78CvIkJv;PQkRF5+3?Wll@E-}_Nu^? 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