Lasso regression
Oh, hi! Today we're going to talk about something called lasso regression. I know that sounds like a big word, but don't worry, I'm going to explain it to you like you're five years old!
Now, have you ever played with a toy lasso before? It's like a rope with a circle at the end that cowboys use to catch animals, like horses or cows. So, in this kind of regression, we will use something that looks like a lasso (a mathematical one, not a real one!) that helps us choose the best variables for our model.
Let me give you an example. Imagine you're trying to predict how tall someone is based on their age, gender, weight, and shoe size. Lasso regression helps us choose which of these variables are the most important, or which variables we should put more emphasis on when trying to make predictions.
The lasso works by putting a limit on how much each variable is allowed to contribute to the prediction. Think of it like giving each variable a set amount of money to donate. The variable that is most important gets to donate the most money, and the less important variables get less and less money to donate.
So in our example, the lasso regression might tell us that age, weight, and shoe size are the most important variables, while gender doesn't really have an impact on predicting someone's height. This helps us make better predictions based on the variables that really matter.
I hope that helps make sense of lasso regression!
Now, have you ever played with a toy lasso before? It's like a rope with a circle at the end that cowboys use to catch animals, like horses or cows. So, in this kind of regression, we will use something that looks like a lasso (a mathematical one, not a real one!) that helps us choose the best variables for our model.
Let me give you an example. Imagine you're trying to predict how tall someone is based on their age, gender, weight, and shoe size. Lasso regression helps us choose which of these variables are the most important, or which variables we should put more emphasis on when trying to make predictions.
The lasso works by putting a limit on how much each variable is allowed to contribute to the prediction. Think of it like giving each variable a set amount of money to donate. The variable that is most important gets to donate the most money, and the less important variables get less and less money to donate.
So in our example, the lasso regression might tell us that age, weight, and shoe size are the most important variables, while gender doesn't really have an impact on predicting someone's height. This helps us make better predictions based on the variables that really matter.
I hope that helps make sense of lasso regression!