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Machine Learning and Computational Statistics, Spring 2017
Homework 2: Lasso Regression: Monday, February 13, 2017, at 10pm (Submit via Grade scope)
Instructions: Your answers to the questions below,
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Any data scientist or researcher who wants to perform feature selection or regularization in their regression analysis can benefit from using lasso regression on GitHub. Lasso regression is particularly useful when dealing with high-dimensional datasets, where it helps to identify the most relevant features and reduce overfitting.
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Lasso regression is a linear regression technique specifically used for feature selection and regularization to prevent overfitting in machine learning models on GitHub.
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Data scientists, machine learning engineers, and researchers who are developing predictive models on GitHub are required to use lasso regression for regularization and feature selection.
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To fill out lasso regression on GitHub, one must provide the dataset, specify the target variable, choose appropriate regularization strength (alpha), and interpret the coefficients of the selected features.
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The purpose of lasso regression on GitHub is to find the most important features in a dataset while penalizing the coefficients of less important features through regularization.
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The information reported on lasso regression on GitHub includes the selected features with non-zero coefficients, the strength of regularization, and the model performance metrics such as R-squared or Mean Squared Error.
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