https://arshadhs.github.io/tags/ols/Recent content in OLS on Arshad SiddiquiHugoen-usSat, 21 Feb 2026 00:00:00 +0000https://arshadhs.github.io/docs/ai/machine-learning/03-ordinary-least-squares/Sat, 21 Feb 2026 00:00:00 +0000https://arshadhs.github.io/docs/ai/machine-learning/03-ordinary-least-squares/<h1 id="direct-solution-method---ordinary-least-squares-and-the-line-of-best-fit"> Direct solution method - Ordinary Least Squares and the Line of Best Fit <a class="anchor" href="#direct-solution-method---ordinary-least-squares-and-the-line-of-best-fit">#</a> </h1> <p>It is possible to compute the best parameters for linear regression <strong>in one shot</strong> (closed-form), instead of iteratively improving them step-by-step. fileciteturn34file10turn34file6</p> <p>For linear regression, the direct method is usually <strong>Ordinary Least Squares (OLS)</strong>.</p> <p>Ordinary Least Squares (OLS) chooses the “best” line by <strong>minimising squared prediction errors</strong>.</p> <blockquote class="book-hint info"> <p>Key takeaway: OLS defines “best fit” as the line that minimises the total squared residual error across all data points.</p>https://arshadhs.github.io/docs/ai/machine-learning/03-cost-function/Sat, 21 Feb 2026 00:00:00 +0000https://arshadhs.github.io/docs/ai/machine-learning/03-cost-function/<h1 id="cost-function"> Cost Function <a class="anchor" href="#cost-function">#</a> </h1> <ul> <li> <p>also known as an objective function</p> </li> <li> <p><strong>how far the predicted values are from the actual ones</strong></p> </li> <li> <p>measure of the difference between predicted values and actual values</p> </li> <li> <p>quantifies the error between a model&rsquo;s predicted values and actual values</p> </li> <li> <p>measures the model’s error on a group of datapoints</p> </li> <li> <p>method used to predict values by drawing the best-fit line through the data</p> </li> <li> <p>used to evaluate the accuracy of a model’s predictions</p>