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Machine_Learning_Sample_Project.html
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Machine_Learning_Sample_Project.html
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<hr />
<hr />
<h2 id="read-data">Read data</h2>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a>Data <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="at">file =</span> <span class="fu">file.choose</span>(), <span class="at">header =</span> <span class="cn">TRUE</span>)</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">head</span>(Data)</span></code></pre></div>
<pre><code>## Sales CompPrice Income Advertising Population Price Age Education US Urban
## 1 9.50 138 73 11 276 120 42 17 1 1
## 2 11.22 111 48 16 260 83 65 10 1 1
## 3 10.06 113 35 10 269 80 59 12 1 1
## 4 7.40 117 100 4 466 97 55 14 1 1
## 5 4.15 141 64 3 340 128 38 13 0 1
## 6 10.81 124 113 13 501 72 78 16 1 0
## ShelveLoc
## 1 1
## 2 3
## 3 2
## 4 2
## 5 1
## 6 1</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>FMdat <span class="ot"><-</span> Data[, <span class="fu">c</span>(<span class="dv">9</span>, <span class="dv">6</span>, <span class="dv">7</span>)]</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">head</span>(FMdat)</span></code></pre></div>
<pre><code>## US Price Age
## 1 1 120 42
## 2 1 83 65
## 3 1 80 59
## 4 1 97 55
## 5 0 128 38
## 6 1 72 78</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>nn <span class="ot"><-</span> <span class="fu">dim</span>(FMdat)[<span class="dv">1</span>]</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>nn</span></code></pre></div>
<pre><code>## [1] 400</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>test_ind <span class="ot"><-</span> <span class="fu">sample</span>(<span class="dv">1</span><span class="sc">:</span>nn, <span class="at">size =</span> <span class="dv">400</span> <span class="sc">*</span> <span class="dv">30</span> <span class="sc">/</span> <span class="dv">100</span>, <span class="at">replace =</span> <span class="cn">FALSE</span>)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>dtrain <span class="ot"><-</span> FMdat[<span class="sc">-</span>test_ind, ]</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>dtest <span class="ot"><-</span> FMdat[test_ind, ]</span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="fu">dim</span>(dtrain)</span></code></pre></div>
<pre><code>## [1] 280 3</code></pre>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">dim</span>(dtest)</span></code></pre></div>
<pre><code>## [1] 120 3</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>ytest <span class="ot"><-</span> dtest<span class="sc">$</span>US</span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a></span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>acc_error <span class="ot"><-</span> <span class="cf">function</span>(tab) {</span>
<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a> b <span class="ot"><-</span> <span class="fu">sum</span>(tab)</span>
<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a> DIM <span class="ot"><-</span> <span class="fu">dim</span>(tab)</span>
<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a> <span class="cf">if</span> (DIM[<span class="dv">1</span>] <span class="sc">==</span> <span class="dv">2</span> <span class="sc">&&</span> DIM[<span class="dv">2</span>] <span class="sc">==</span> <span class="dv">2</span>) {</span>
<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a> a <span class="ot">=</span> tab[<span class="dv">1</span>, <span class="dv">1</span>] <span class="sc">+</span> tab[<span class="dv">2</span>, <span class="dv">2</span>]</span>
<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a> acc <span class="ot">=</span> a<span class="sc">/</span>b; error <span class="ot">=</span> <span class="dv">1</span> <span class="sc">-</span> acc</span>
<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a> result <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Acc"</span> <span class="ot">=</span> acc, <span class="st">"Error"</span> <span class="ot">=</span> error)</span>
<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a> } <span class="cf">else</span> {</span>
<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a> a <span class="ot">=</span> <span class="fu">sum</span>(<span class="fu">diag</span>(tab))</span>
<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a> result <span class="ot">=</span> <span class="fu">c</span>(<span class="st">"Acc"</span> <span class="ot">=</span> a <span class="sc">/</span> b, <span class="st">"Error"</span> <span class="ot">=</span> <span class="dv">1</span> <span class="sc">-</span> a<span class="sc">/</span>b)</span>
<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a> }</span>
<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a> result</span>
<span id="cb11-15"><a href="#cb11-15" tabindex="-1"></a>}</span></code></pre></div>
<hr />
<hr />
<h1 id="classification--">Classification ————-</h1>
<hr />
<hr />
<h2 id="id_1--svm-model-">1- SVM Model —————</h2>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">library</span>(e1071)</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="do">## define Model ------------------</span></span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">444</span>)</span>
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a>Model_Svm <span class="ot"><-</span> <span class="fu">svm</span>(<span class="fu">factor</span>(US) <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain, </span>
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a><span class="at">kernel =</span> <span class="st">"radial"</span>, <span class="at">gamma =</span> <span class="fl">1.5</span>, </span>
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a><span class="at">cost =</span> <span class="dv">1</span>, <span class="at">decision.values =</span> <span class="cn">TRUE</span>)</span>
<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a><span class="fu">summary</span>(Model_Svm)</span></code></pre></div>
<pre><code>##
## Call:
## svm(formula = factor(US) ~ Price + Age, data = dtrain, kernel = "radial",
## gamma = 1.5, cost = 1, decision.values = TRUE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 216
##
## ( 120 96 )
##
##
## Number of Classes: 2
##
## Levels:
## 0 1</code></pre>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>pred_LABEL_svm <span class="ot"><-</span> <span class="fu">predict</span>(Model_Svm, <span class="at">newdata =</span> dtest)</span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a></span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>cfMat <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">preds =</span> pred_LABEL_svm)</span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>cfMat</span></code></pre></div>
<pre><code>## preds
## real 0 1
## 0 1 45
## 1 1 73</code></pre>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>res_svm <span class="ot"><-</span> <span class="fu">acc_error</span>(cfMat)</span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a>res_svm</span></code></pre></div>
<pre><code>## Acc Error
## 0.6166667 0.3833333</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>pred_SCORE_svm <span class="ot"><-</span> <span class="fu">attributes</span>(</span>
<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a> <span class="fu">predict</span>(Model_Svm, dtest, </span>
<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a> <span class="at">decision.values =</span> <span class="cn">TRUE</span>) )<span class="sc">$</span>decision.values</span></code></pre></div>
<hr />
<hr />
<h2 id="id_2--tree-model--">2- Tree Model ——————-</h2>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">library</span>(rpart)</span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="fu">library</span>(rpart.plot)</span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>Model_Tree <span class="ot"><-</span> <span class="fu">rpart</span>(<span class="fu">factor</span>(US) <span class="sc">~</span> Price <span class="sc">+</span> Age, </span>
<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a><span class="at">data =</span> dtrain, <span class="at">parms =</span> <span class="fu">list</span>(<span class="at">split =</span> <span class="st">"information"</span>), </span>
<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a><span class="at">method =</span> <span class="st">"class"</span>)</span>
<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a></span>
<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a><span class="co"># summary(Model_Tree)</span></span>
<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a><span class="do">##########################</span></span>
<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a></span>
<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a>pred_LABEL_tree <span class="ot"><-</span> <span class="fu">predict</span>(Model_Tree, dtest, <span class="at">type =</span> <span class="st">"class"</span>)</span>
<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a>pred_SCORE_tree <span class="ot"><-</span> <span class="fu">predict</span>(Model_Tree, dtest, <span class="at">type =</span> <span class="st">"prob"</span>)[, <span class="dv">2</span>]</span>
<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a>cfMat <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_tree)</span>
<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a>cfMat</span></code></pre></div>
<pre><code>## pred
## real 0 1
## 0 12 34
## 1 16 58</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>res_tree <span class="ot"><-</span> <span class="fu">acc_error</span>(cfMat)</span>
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>res_tree</span></code></pre></div>
<pre><code>## Acc Error
## 0.5833333 0.4166667</code></pre>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">rpart.plot</span>(Model_Tree)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<hr />
<h2 id="id_3--boosting-model-">3- Boosting Model —————</h2>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="fu">library</span>(gbm)</span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>Model_boost <span class="ot"><-</span> <span class="fu">gbm</span>(US <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain, </span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a><span class="at">distribution =</span> <span class="st">"bernoulli"</span>, </span>
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a><span class="at">shrinkage =</span> <span class="fl">0.0001</span>)</span>
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a>pred_SCORE_boost <span class="ot"><-</span> <span class="fu">predict</span>(Model_boost, </span>
<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a><span class="at">newdata =</span> dtest, <span class="at">type =</span> <span class="st">"response"</span>)</span>
<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a>pred_LABEL_boost <span class="ot"><-</span> (pred_SCORE_boost <span class="sc">></span> <span class="fl">0.5</span>) <span class="sc">/</span> <span class="dv">1</span></span>
<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a>cfMat <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, </span>
<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a><span class="at">preds =</span> pred_LABEL_boost)</span>
<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a>cfMat</span></code></pre></div>
<pre><code>## preds
## real 1
## 0 46
## 1 74</code></pre>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>res_boost1 <span class="ot"><-</span> <span class="fu">acc_error</span>(cfMat)</span>
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>res_boost1</span></code></pre></div>
<pre><code>## Acc Error
## 0.3833333 0.6166667</code></pre>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="do">## better cutof</span></span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a></span>
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a><span class="fu">library</span>(ROCR)</span>
<span id="cb28-4"><a href="#cb28-4" tabindex="-1"></a></span>
<span id="cb28-5"><a href="#cb28-5" tabindex="-1"></a>train_pred <span class="ot"><-</span> <span class="fu">predict</span>(Model_boost, <span class="at">type =</span> <span class="st">"response"</span>)</span>
<span id="cb28-6"><a href="#cb28-6" tabindex="-1"></a>yt <span class="ot"><-</span> dtrain<span class="sc">$</span>US</span>
<span id="cb28-7"><a href="#cb28-7" tabindex="-1"></a>temp1 <span class="ot"><-</span> <span class="fu">predict</span>(Model_boost, <span class="at">type =</span> <span class="st">"response"</span>)</span>
<span id="cb28-8"><a href="#cb28-8" tabindex="-1"></a>temp2 <span class="ot"><-</span> <span class="fu">prediction</span>(temp1, yt) </span>
<span id="cb28-9"><a href="#cb28-9" tabindex="-1"></a>cost_perf <span class="ot">=</span> <span class="fu">performance</span>(temp2, <span class="st">"cost"</span>)</span>
<span id="cb28-10"><a href="#cb28-10" tabindex="-1"></a>Cut_p <span class="ot"><-</span> cost_perf<span class="sc">@</span>x.values[[<span class="dv">1</span>]][<span class="fu">which.min</span>(cost_perf<span class="sc">@</span>y.values[[<span class="dv">1</span>]])] </span>
<span id="cb28-11"><a href="#cb28-11" tabindex="-1"></a>Cut_p</span></code></pre></div>
<pre><code>## [1] 0.6566555</code></pre>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>pred_LABEL_boost2 <span class="ot"><-</span> (pred_SCORE_boost <span class="sc">></span> Cut_p) <span class="sc">/</span> <span class="dv">1</span> </span>
<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_boost2)</span>
<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## pred
## real 0 1
## 0 3 43
## 1 1 73</code></pre>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a>res_boost_2 <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>res_boost_2</span></code></pre></div>
<pre><code>## Acc Error
## 0.6333333 0.3666667</code></pre>
<hr />
<hr />
<h2 id="id_4--logistic-model-">4- Logistic Model —————–</h2>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a>Model_log <span class="ot"><-</span> <span class="fu">glm</span>(US <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain,</span>
<span id="cb34-3"><a href="#cb34-3" tabindex="-1"></a><span class="at">family =</span> binomial)</span>
<span id="cb34-4"><a href="#cb34-4" tabindex="-1"></a>pred_SCORE_log <span class="ot"><-</span> <span class="fu">predict</span>(Model_log, dtest, </span>
<span id="cb34-5"><a href="#cb34-5" tabindex="-1"></a><span class="at">type =</span> <span class="st">"response"</span>)</span>
<span id="cb34-6"><a href="#cb34-6" tabindex="-1"></a>pred_LABEL_log <span class="ot"><-</span> (pred_SCORE_log <span class="sc">></span> <span class="fl">0.5</span>) <span class="sc">/</span> <span class="dv">1</span></span>
<span id="cb34-7"><a href="#cb34-7" tabindex="-1"></a></span>
<span id="cb34-8"><a href="#cb34-8" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_log) </span>
<span id="cb34-9"><a href="#cb34-9" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## pred
## real 1
## 0 46
## 1 74</code></pre>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a>res_log <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a>res_log</span></code></pre></div>
<pre><code>## Acc Error
## 0.3833333 0.6166667</code></pre>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a><span class="do">## change cutpoint </span></span>
<span id="cb38-2"><a href="#cb38-2" tabindex="-1"></a></span>
<span id="cb38-3"><a href="#cb38-3" tabindex="-1"></a></span>
<span id="cb38-4"><a href="#cb38-4" tabindex="-1"></a>temp1 <span class="ot"><-</span> <span class="fu">predict</span>(Model_log, <span class="at">type =</span> <span class="st">"response"</span>)</span>
<span id="cb38-5"><a href="#cb38-5" tabindex="-1"></a>temp2 <span class="ot"><-</span> <span class="fu">prediction</span>(temp1, dtrain<span class="sc">$</span>US) </span>
<span id="cb38-6"><a href="#cb38-6" tabindex="-1"></a>cost_perf <span class="ot">=</span> <span class="fu">performance</span>(temp2, <span class="st">"cost"</span>)</span>
<span id="cb38-7"><a href="#cb38-7" tabindex="-1"></a>Cut_p <span class="ot"><-</span> cost_perf<span class="sc">@</span>x.values[[<span class="dv">1</span>]][<span class="fu">which.min</span>(cost_perf<span class="sc">@</span>y.values[[<span class="dv">1</span>]])] </span>
<span id="cb38-8"><a href="#cb38-8" tabindex="-1"></a>Cut_p</span></code></pre></div>
<pre><code>## 47
## 0.5956299</code></pre>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a>pred_LABEL_log2 <span class="ot"><-</span> (pred_SCORE_log <span class="sc">></span> Cut_p) <span class="sc">/</span> <span class="dv">1</span> </span>
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_log2)</span>
<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## pred
## real 0 1
## 0 4 42
## 1 0 74</code></pre>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a>res_log_2 <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>res_log_2</span></code></pre></div>
<pre><code>## Acc Error
## 0.65 0.35</code></pre>
<hr />
<hr />
<h2 id="id_5--qda-model-">5- QDA Model ————————</h2>
<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" tabindex="-1"></a><span class="fu">library</span>(MASS)</span>
<span id="cb44-2"><a href="#cb44-2" tabindex="-1"></a>Model_qda <span class="ot"><-</span> <span class="fu">qda</span>(US <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain)</span>
<span id="cb44-3"><a href="#cb44-3" tabindex="-1"></a>pred <span class="ot"><-</span> <span class="fu">predict</span>(Model_qda, <span class="at">newdata =</span> dtest, </span>
<span id="cb44-4"><a href="#cb44-4" tabindex="-1"></a><span class="at">type =</span> <span class="st">"class"</span>)</span>
<span id="cb44-5"><a href="#cb44-5" tabindex="-1"></a></span>
<span id="cb44-6"><a href="#cb44-6" tabindex="-1"></a>pred_LABEL_qda <span class="ot"><-</span> pred<span class="sc">$</span>class</span>
<span id="cb44-7"><a href="#cb44-7" tabindex="-1"></a>pred_SCORE_qda <span class="ot"><-</span> pred<span class="sc">$</span>posterior[, <span class="dv">2</span>]</span>
<span id="cb44-8"><a href="#cb44-8" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_qda)</span>
<span id="cb44-9"><a href="#cb44-9" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## pred
## real 0 1
## 0 3 43
## 1 0 74</code></pre>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a>res_qda <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb46-2"><a href="#cb46-2" tabindex="-1"></a>res_qda</span></code></pre></div>
<pre><code>## Acc Error
## 0.6416667 0.3583333
***
***
## 6- RandomForest Model -----------
```r
set.seed(111)
library(randomForest)
Model_rf <- randomForest(factor(US) ~ Price + Age,
data = dtrain,
ntree = 300)
pred_LABEL_rf <- predict(Model_rf,
dtest, type = "response")
pred_SCORE_rf <- predict(Model_rf,
dtest, type = "prob")[, 2]
tab <- table(real = ytest,
preds = pred_LABEL_rf)
tab
## preds
## real 0 1
## 0 12 34
## 1 15 59</code></pre>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a>res_rf <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a>res_rf</span></code></pre></div>
<pre><code>## Acc Error
## 0.5916667 0.4083333</code></pre>
<hr />
<hr />
<h2 id="id_7--lda-model--">7- LDA Model ——————-</h2>
<div class="sourceCode" id="cb50"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" tabindex="-1"></a><span class="fu">library</span>(MASS)</span>
<span id="cb50-2"><a href="#cb50-2" tabindex="-1"></a>Model_lda <span class="ot"><-</span> <span class="fu">lda</span>(US <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain)</span>
<span id="cb50-3"><a href="#cb50-3" tabindex="-1"></a>pred <span class="ot"><-</span> <span class="fu">predict</span>(Model_lda, <span class="at">newdata =</span> dtest, <span class="at">type =</span> <span class="st">"class"</span>)</span>
<span id="cb50-4"><a href="#cb50-4" tabindex="-1"></a>pred_LABEL_lda <span class="ot"><-</span> pred<span class="sc">$</span>class</span>
<span id="cb50-5"><a href="#cb50-5" tabindex="-1"></a>pred_SCORE_lda <span class="ot"><-</span> pred<span class="sc">$</span>posterior[, <span class="dv">2</span>]</span>
<span id="cb50-6"><a href="#cb50-6" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(pred_LABEL_lda, ytest)</span>
<span id="cb50-7"><a href="#cb50-7" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## ytest
## pred_LABEL_lda 0 1
## 0 0 0
## 1 46 74</code></pre>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" tabindex="-1"></a>res_lda <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb52-2"><a href="#cb52-2" tabindex="-1"></a></span>
<span id="cb52-3"><a href="#cb52-3" tabindex="-1"></a>res_lda</span></code></pre></div>
<pre><code>## Acc Error
## 0.6166667 0.3833333</code></pre>
<hr />
<hr />
<h2 id="id_8--bagging--">8- bagging ——————-</h2>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" tabindex="-1"></a><span class="fu">library</span>(caret)</span>
<span id="cb54-2"><a href="#cb54-2" tabindex="-1"></a>cvcontrol <span class="ot"><-</span> <span class="fu">trainControl</span>(<span class="at">method=</span><span class="st">"repeatedcv"</span>, <span class="at">number =</span> <span class="dv">5</span>,</span>
<span id="cb54-3"><a href="#cb54-3" tabindex="-1"></a> <span class="at">allowParallel=</span><span class="cn">TRUE</span>)</span>
<span id="cb54-4"><a href="#cb54-4" tabindex="-1"></a>Model_bag <span class="ot"><-</span> <span class="fu">train</span>(<span class="fu">factor</span>(US) <span class="sc">~</span> Price <span class="sc">+</span> Age, <span class="at">data =</span> dtrain, </span>
<span id="cb54-5"><a href="#cb54-5" tabindex="-1"></a><span class="at">method =</span> <span class="st">"treebag"</span>, </span>
<span id="cb54-6"><a href="#cb54-6" tabindex="-1"></a><span class="at">trControl =</span> cvcontrol, </span>
<span id="cb54-7"><a href="#cb54-7" tabindex="-1"></a><span class="at">importance =</span> <span class="cn">TRUE</span>) </span>
<span id="cb54-8"><a href="#cb54-8" tabindex="-1"></a></span>
<span id="cb54-9"><a href="#cb54-9" tabindex="-1"></a>Model_bag</span></code></pre></div>
<pre><code>## Bagged CART
##
## 280 samples
## 2 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 1 times)
## Summary of sample sizes: 224, 225, 224, 223, 224
## Resampling results:
##
## Accuracy Kappa
## 0.5606038 -0.01540645</code></pre>
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" tabindex="-1"></a>pred_SCORE_bag <span class="ot"><-</span> <span class="fu">predict</span>(Model_bag, dtest, </span>
<span id="cb56-2"><a href="#cb56-2" tabindex="-1"></a><span class="at">type =</span> <span class="st">"prob"</span>)[, <span class="st">'1'</span>]</span>
<span id="cb56-3"><a href="#cb56-3" tabindex="-1"></a></span>
<span id="cb56-4"><a href="#cb56-4" tabindex="-1"></a>pred_LABEL_bag <span class="ot"><-</span> <span class="fu">predict</span>(Model_bag, dtest, <span class="at">type =</span> <span class="st">"raw"</span>)</span>
<span id="cb56-5"><a href="#cb56-5" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">real =</span> ytest, <span class="at">pred =</span> pred_LABEL_bag)</span>
<span id="cb56-6"><a href="#cb56-6" tabindex="-1"></a>tab</span></code></pre></div>
<pre><code>## pred
## real 0 1
## 0 10 36
## 1 20 54</code></pre>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" tabindex="-1"></a>res_bag <span class="ot"><-</span> <span class="fu">acc_error</span>(tab)</span>
<span id="cb58-2"><a href="#cb58-2" tabindex="-1"></a>res_bag</span></code></pre></div>
<pre><code>## Acc Error
## 0.5333333 0.4666667</code></pre>
<hr />
<hr />
<h2 id="results-">Results —————</h2>
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" tabindex="-1"></a><span class="fu">library</span>(pROC)</span>
<span id="cb60-2"><a href="#cb60-2" tabindex="-1"></a>roc_bag <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_bag)</span>
<span id="cb60-3"><a href="#cb60-3" tabindex="-1"></a>roc_log <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_log)</span>
<span id="cb60-4"><a href="#cb60-4" tabindex="-1"></a>roc_lda <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_lda)</span>
<span id="cb60-5"><a href="#cb60-5" tabindex="-1"></a>roc_qda <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_qda)</span>
<span id="cb60-6"><a href="#cb60-6" tabindex="-1"></a>roc_rf <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_rf)</span>
<span id="cb60-7"><a href="#cb60-7" tabindex="-1"></a>roc_boost <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_boost)</span>
<span id="cb60-8"><a href="#cb60-8" tabindex="-1"></a>roc_svm <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_svm)</span>
<span id="cb60-9"><a href="#cb60-9" tabindex="-1"></a>roc_tree <span class="ot"><-</span> <span class="fu">roc</span>(ytest, pred_SCORE_tree)</span>
<span id="cb60-10"><a href="#cb60-10" tabindex="-1"></a><span class="fu">plot</span>(roc_bag, <span class="at">main =</span> <span class="st">"ROC"</span>, <span class="at">col =</span> <span class="dv">1</span>)</span>
<span id="cb60-11"><a href="#cb60-11" tabindex="-1"></a><span class="fu">plot</span>(roc_log, <span class="at">col =</span> <span class="dv">2</span>, <span class="at">add =</span> T)</span>
<span id="cb60-12"><a href="#cb60-12" tabindex="-1"></a><span class="fu">plot</span>(roc_lda, <span class="at">col =</span> <span class="dv">3</span>, <span class="at">add =</span> T)</span>
<span id="cb60-13"><a href="#cb60-13" tabindex="-1"></a><span class="fu">plot</span>(roc_qda, <span class="at">col =</span> <span class="dv">4</span>, <span class="at">add =</span> T)</span>
<span id="cb60-14"><a href="#cb60-14" tabindex="-1"></a><span class="fu">plot</span>(roc_rf, <span class="at">col =</span> <span class="dv">5</span>, <span class="at">add =</span> T)</span>
<span id="cb60-15"><a href="#cb60-15" tabindex="-1"></a><span class="fu">plot</span>(roc_boost, <span class="at">col =</span> <span class="dv">6</span>, <span class="at">add =</span> T)</span>
<span id="cb60-16"><a href="#cb60-16" tabindex="-1"></a><span class="fu">plot</span>(roc_svm, <span class="at">col =</span> <span class="dv">7</span>, <span class="at">add =</span> T)</span>
<span id="cb60-17"><a href="#cb60-17" tabindex="-1"></a><span class="fu">plot</span>(roc_tree, <span class="at">col =</span> <span class="dv">8</span>, <span class="at">add =</span> T)</span>
<span id="cb60-18"><a href="#cb60-18" tabindex="-1"></a><span class="fu">legend</span>(<span class="st">"bottomright"</span>, </span>
<span id="cb60-19"><a href="#cb60-19" tabindex="-1"></a><span class="at">legend =</span> <span class="fu">c</span>(<span class="st">"Bagging"</span>, <span class="st">"Logistic"</span>, <span class="st">"LDA"</span>, </span>
<span id="cb60-20"><a href="#cb60-20" tabindex="-1"></a><span class="st">"QDA"</span>, <span class="st">"Randomforest"</span>, <span class="st">"SVM"</span>, <span class="st">"LDA"</span>, <span class="st">"QDA"</span>), </span>
<span id="cb60-21"><a href="#cb60-21" tabindex="-1"></a><span class="at">col =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">8</span>, <span class="at">lwd =</span> <span class="dv">2</span>, <span class="at">lty =</span> <span class="dv">1</span>, <span class="at">bty =</span> <span class="st">"n"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" tabindex="-1"></a>Models <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"bagging"</span>, <span class="st">"logistic"</span>, <span class="st">"LDA"</span>, <span class="st">"QDA"</span>, </span>
<span id="cb61-2"><a href="#cb61-2" tabindex="-1"></a><span class="st">"Randomforest"</span>, <span class="st">"Boosting"</span>, <span class="st">"SVM"</span>, <span class="st">"TREE"</span>)</span>
<span id="cb61-3"><a href="#cb61-3" tabindex="-1"></a></span>
<span id="cb61-4"><a href="#cb61-4" tabindex="-1"></a>AUC <span class="ot"><-</span> <span class="fu">c</span>(<span class="fu">auc</span>(roc_bag), <span class="fu">auc</span>(roc_log), <span class="fu">auc</span>(roc_lda), </span>
<span id="cb61-5"><a href="#cb61-5" tabindex="-1"></a><span class="fu">auc</span>(roc_qda), <span class="fu">auc</span>(roc_rf), <span class="fu">auc</span>(roc_boost), <span class="fu">auc</span>(roc_svm), </span>
<span id="cb61-6"><a href="#cb61-6" tabindex="-1"></a><span class="fu">auc</span>(roc_tree))</span>
<span id="cb61-7"><a href="#cb61-7" tabindex="-1"></a></span>
<span id="cb61-8"><a href="#cb61-8" tabindex="-1"></a>ERROR <span class="ot">=</span> <span class="fu">c</span>(res_bag[<span class="dv">2</span>], res_log_2[<span class="dv">2</span>], res_lda[<span class="dv">2</span>], </span>
<span id="cb61-9"><a href="#cb61-9" tabindex="-1"></a>res_qda[<span class="dv">2</span>], res_rf[<span class="dv">2</span>], res_boost_2[<span class="dv">2</span>], res_svm[<span class="dv">2</span>], res_tree[<span class="dv">2</span>])</span>
<span id="cb61-10"><a href="#cb61-10" tabindex="-1"></a></span>
<span id="cb61-11"><a href="#cb61-11" tabindex="-1"></a>data_result <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">Model =</span> Models, </span>
<span id="cb61-12"><a href="#cb61-12" tabindex="-1"></a><span class="at">AUC =</span> AUC, <span class="at">Error =</span> ERROR)</span>
<span id="cb61-13"><a href="#cb61-13" tabindex="-1"></a></span>
<span id="cb61-14"><a href="#cb61-14" tabindex="-1"></a>knitr <span class="sc">::</span> <span class="fu">kable</span>(data_result, <span class="at">caption =</span> <span class="st">"RESULTS"</span>, <span class="at">align =</span> <span class="st">"c"</span>)</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="center">Model</th>
<th align="center">AUC</th>
<th align="center">Error</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">bagging</td>
<td align="center">0.5051410</td>
<td align="center">0.4666667</td>
</tr>
<tr class="even">
<td align="center">logistic</td>
<td align="center">0.5133666</td>
<td align="center">0.3500000</td>
</tr>
<tr class="odd">
<td align="center">LDA</td>
<td align="center">0.5127791</td>
<td align="center">0.3833333</td>
</tr>
<tr class="even">
<td align="center">QDA</td>
<td align="center">0.5653643</td>
<td align="center">0.3583333</td>
</tr>
<tr class="odd">
<td align="center">Randomforest</td>
<td align="center">0.5242362</td>
<td align="center">0.4083333</td>
</tr>