Feb 8, 2025 · 5 min read · Linear Regression is one of the most common machine learning algorithms. It is especially used in regression problems in machine learning. This article will help you deduce the depths of this algorithm and will make your understanding of it clearer. ...
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Oct 26, 2024 · 4 min read · When working with linear regression, the goal is to identify the best-fit line that captures the relationship between your input (independent) variable x and output (dependent) variable y. This line, represented by the equation: $$h_{\theta}(x) = \th...
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Oct 26, 2024 · 5 min read · When it comes to evaluating machine learning models, two key concepts stand out: residuals and cost functions. These terms play a crucial role in determining how well our model predicts outcomes. In this blog post, we will explore these concepts in d...
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Oct 26, 2024 · 4 min read · When building predictive models, especially in regression tasks, evaluating their performance is crucial. This is where error metrics come into play. In this blog post, we will explore three common error metrics: Mean Squared Error (MSE), Mean Absolu...
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Oct 23, 2023 · 1 min read · In linear regression, the cost function, also known as the loss function or the mean squared error (MSE) function, is used to measure the error or the dissimilarity between the predicted values and the actual target values. The goal of linear regress...
Join discussionJan 10, 2022 · 4 min read · Function optimization done right Photo by Armand Khoury on Unsplash BFGS falls into the category of optimization algorithms based on Quasi-Newton approaches. Where and how it is used BFGS is a type of second order optimization algorithm. BFGS has us...
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