Linear and Logistic Regression with L1 and L2 ( Lasso and Ridge) Regularization Feature Selection

KGP Talkie
KGP Talkie
14 هزار بار بازدید - 5 سال پیش - In this video, we will
In this video, we will learn how to use linear and logistic regression coefficients with Lasso and Ridge Regularization for feature selection in Machine learning. Let’s first understand what exactly linear regression is, it is a straightforward approach to predict the response y on the basis of different prediction variables such as x and ε. There is a linear relation between x and y.𝑦𝑖 = 𝛽0 + 𝛽1.𝑋𝑖 + 𝜀𝑖. Basic assumptions are that linear relationship with the target y, feature space X should have gaussian distribution, features are not correlated with other features, features are in the same scale-like have some variance.

🔊 Watch till last for a detailed description
02:58 What is linear regression?
07:52 What is LASSO regularisation?
13:09 What is Ridge regularisation?
17:05 Load the dataset
21:18 Estimation of coefficients of linear regression
31:53 Logistic regression coefficient with L1 regularization
39:04 L2 regularization

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5 سال پیش در تاریخ 1398/06/03 منتشر شده است.
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