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A Beginner’s Guide to Ridge Regression in Machine Learning

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Introduction Regression analysis is a fundamental technique in machine learning, used to predict a dependent variable based on one or more independent variables. However, traditional regression methods, such as simple linear regression, can struggle to deal with multicollinearity (high correlation between predictors). This is where ridge regression comes in handy. Ridge regression is an advanced form of linear regression that reduces overfitting by adding a penalty term to the model. In this article, we will cover what ridge regression is, why it is important, how it works, its assumptions, and how to implement it using Python. What is Ridge Regression? Ridge regression is a type of regularization technique that modifies the linear regression model by adding an L2 penalty term to the cost function. This penalty prevents the model from giving a large weight to any single predictor, which helps reduce overfitting.  Mathematically, the ridge regression cost function is: where: is the ...
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The A-Z Guide to Gradient Descent Algorithm and Its Variants

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What is Gradient Descent? Gradient descent is an efficient first-order optimization algorithm for finding a differentiable function's global or local minimum. It estimates the values of parameters or coefficients that minimize a cost function.  The gradient descent method has proved to be especially useful as it can be adopted in spaces of any number of dimensions. The gradient descent method can be used when parameters cannot be calculated analytically and is a good choice for the differentiable cost function. How does Gradient Descent work? Image Credit: Neural Networks and Deep Learning To get an intuitive idea of how Gradient Descent works, let us consider the entire range of values the parameters can take. Here the axes w and b represent the range of values the parameters w and b can take, respectively. In this case, these parameters express a simple linear unit. Hence, the curved surface shown represents the cost function J(w, b) would vary for different values of w and ...
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Regression metrics in machine learning

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 Regression metrics help us evaluate the performance of regression models in machine learning. For beginners, understanding these parameters is important for model selection and optimization. In this article, we will focus on the important regression metrics: MAE, MSE, RMSE, R² score, and adjusted R² score.   Each section is written in list format for better clarity and understanding.   1. Mean Absolute Error (MAE)   MAE calculates the average of absolute differences between predicted and actual values.   formula:      Important points:   1. Easy to understand: MAE is easy to understand and calculate.   2. Same unit as the target variable: The errors are in the same unit as the target variable.   3. Not sensitive to outliers: Large errors do not affect MAE as much as they do MSE.   Use cases:   When you need a simple and descriptive metric for error measurement.   Python code:   import mean_absolute_error fr...
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