Tag: #ImproveModelPerformance
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Effective Feature Selection Techniques for Improved Model Performance
Introduction Feature selection is a crucial step in building machine learning models, as irrelevant or redundant features can hinder model performance. In this blog post, we will explore two essential feature selection methods and apply them to a real-world dataset: eliminating low variance features and recursive feature elimination using cross-validation. Eliminating Low Variance Features: One…
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Unveiling the Power of Principal Component Analysis (PCA)
Introduction In the vast landscape of machine learning, we’ve delved into supervised learning methods for predicting labels based on labeled training data. Now, let’s embark on a journey into the realm of unsupervised learning. Here, the focus is on algorithms that uncover intriguing aspects of data without relying on any known labels. One such workhorse…