Forum MenúNavegación del ForoForoActividadMigajas del Foro - Te encuentras aquí:Proyectos Colaborativos del Estado de SinaloaLA BASURA: FORO IExplain Principal Component Analy …Publicar MensajePublicar Mensaje: Explain Principal Component Analysis (PCA)? <blockquote><div class="quotetitle">Citando a Invitado del 25 octubre, 2024, 2:49 am</div>Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and data analysis. It transforms a dataset into a new coordinate system, where the greatest variance in the data lies along the first coordinate (or principal component), the second greatest variance along the second coordinate, and so on. <h3>Key Steps in PCA:</h3> <ol> <li><strong>Standardization</strong>: <ul> <li>Scale the data so that each feature contributes equally to the analysis. This is typically done by subtracting the mean and dividing by the standard deviation for each feature.</li> </ul> </li> <li><strong>Covariance Matrix Computation</strong>: <ul> <li>Calculate the covariance matrix to understand how the features vary with respect to each other. This matrix captures the relationships between different features.</li> </ul> </li> <li><strong>Eigenvalue and Eigenvector Calculation</strong>: <ul> <li>Compute the eigenvalues and eigenvectors of the covariance matrix. Eigenvectors represent the directions of the new feature space (principal components), while eigenvalues indicate the amount of variance captured by each principal component.</li> </ul> </li> <li><strong>Sort Eigenvalues</strong>: <ul> <li>Rank the eigenvalues in descending order and select the top <span class="katex"><span class="katex-mathml">kk</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">k</span></span></span></span> eigenvectors that correspond to the largest eigenvalues. This selection determines the number of principal components to retain.</li> </ul> </li> <li><strong>Transformation</strong>: <ul> <li>Project the original data onto the new feature space defined by the selected principal components. This results in a lower-dimensional representation of the data.</li> </ul> </li> </ol> <div class="flex max-w-full flex-col flex-grow"> <div class="min-h-8 text-message flex w-full flex-col items-end gap-2 whitespace-normal break-words [.text-message+&]:mt-5" dir="auto" data-message-author-role="assistant" data-message-id="7bcd7cea-512f-47f6-8f7b-24342460f3f8" data-message-model-slug="gpt-4o-mini"> <div class="flex w-full flex-col gap-1 empty:hidden first:pt-[3px]"> <div class="markdown prose w-full break-words dark:prose-invert light"> Enroll in a comprehensive <a href="https://www.sevenmentor.com/machine-learning-course-in-pune.php"><strong>Machine Learning course in Pune</strong></a> to gain hands-on experience, expert guidance, and the skills needed to excel in this dynamic field. </div> </div> </div> </div> <div class="mb-2 flex gap-3 empty:hidden -ml-2"> <div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute flex"> <div class="flex items-center"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary" aria-label="Copy" data-testid="copy-turn-action-button"></button> <div class="flex items-center pb-0"></div> </div> </div> </div></blockquote><br> Cancelar
