deploy: f1c7818

_sources/demo_notebooks/quickstart_example.ipynb

@@ -118,7 +118,7 @@
     "\n",
     "$\\beta = {(X_{train}^TX_{train})}^{-1}X_{train}^Ty_{train}$\n",
     "\n",
-    "$\\hat{y} = X_{input}^T \\beta$\n",
+    "$\\hat{y} = X_{input} \\beta$\n",
     "\n",
     "where $X_{train}$ and $X_{input}$ correspond to the training data and the input data you would like to inference on, respectively. $X_{train}$ and $X_{input}$ both have a column of ones concatenated to the feature space for the bias.\n"
    ]

demo_notebooks/quickstart_example.html

@@ -490,7 +490,7 @@ <h2>Train constant prediction model<a class="headerlink" href="#train-constant-p
 <section id="train-multiple-linear-regression-model">
 <h2>Train multiple linear regression model<a class="headerlink" href="#train-multiple-linear-regression-model" title="Permalink to this heading">#</a></h2>
 <p><span class="math notranslate nohighlight">\(\beta = {(X_{train}^TX_{train})}^{-1}X_{train}^Ty_{train}\)</span></p>
-<p><span class="math notranslate nohighlight">\(\hat{y} = X_{input}^T \beta\)</span></p>
+<p><span class="math notranslate nohighlight">\(\hat{y} = X_{input} \beta\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(X_{train}\)</span> and <span class="math notranslate nohighlight">\(X_{input}\)</span> correspond to the training data and the input data you would like to inference on, respectively. <span class="math notranslate nohighlight">\(X_{train}\)</span> and <span class="math notranslate nohighlight">\(X_{input}\)</span> both have a column of ones concatenated to the feature space for the bias.</p>
 <section id="adding-bias-unit">
 <h3>adding bias unit<a class="headerlink" href="#adding-bias-unit" title="Permalink to this heading">#</a></h3>