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<p class="kicker">Data Driven World</p>
<h1>Classification Categorical Data</h1>
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<a class="active" href="index.html">Home</a>
<a href="simple-sigmoid.html">Simple Sigmoid</a>
<a href="confusion-matrix.html">Confusion Matrix (Page 3)</a>
<a href="sigmoid.html">Sigmoid Function</a>
<a href="cost-visualization.html">Cost Function</a>
<a href="about.html">Notes</a>
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<h2>Lecture Objective</h2>
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The objective of this lecture is classification of categorical data. Use these interactive pages to understand
how logistic regression and threshold-based decisions classify observations into categories.
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<h3>Simple Sigmoid (Page 1)</h3>
<p>Basic illustration of the function 1/(1+np.exp(-z)) with a single z control.</p>
<a class="cta-button" href="simple-sigmoid.html">Simple Sigmoid</a>
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<h3>Confusion Matrix Practice (Page 3)</h3>
<p>Practice threshold-based 2-class and 3-class predictions, then build confusion matrix and key metrics.</p>
<a class="cta-button secondary" href="confusion-matrix.html">Confusion Matrix</a>
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<h3>Sigmoid Function</h3>
<p>Interactive plot of p = 1 / (1 + exp(-(b0 + b1x))) with sliders and chart actions.</p>
<a class="cta-button secondary" href="sigmoid.html">Sigmoid Function</a>
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<h3>Visualization for Cost Function</h3>
<p>Compare two logistic-model cases and track gradient descent trajectory on the cost surface.</p>
<a class="cta-button secondary" href="cost-visualization.html">Cost Function</a>
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