you can use regularization techniques like Ridge or Lasso regression to improve performance, address overfitting, and ensure that your linear regression models remain robust and generalize well to ...

Here, we look at how to use data imported into Microsoft Excel to perform a linear regression and how to interpret the results. Linear regression models the relationship between a dependent and ...

When using linear regression with interactions, technically, it's not necessary to normalize/scale your data. But normalizing usually leads to a better prediction model, especially if some raw ...

Similarly, it also allows non-linear relationships to be modeled using regression. Importantly, a logit model allows us to produce interpretable coefficients where an odds ratio is the change in the ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...

In this module, we will introduce generalized linear models (GLMs ... and when that count is thought to depend on a set of predictors, we can use Poisson regression as a model. We will describe the ...

Here, we look at how to use data imported into Microsoft Excel to perform a linear regression and how to interpret the results. Linear regression models the relationship between a dependent and ...