#How to turn your dog’s nap time into a regularized linear model

#How to turn your dog’s nap time into a regularized linear model

Looking at this nap duration model, Beta 0 is the intercept, the value the target takes when all features are equal to zero.

The remaining betas are the unknown coefficients which, along with the intercept, are the missing pieces of the model. You can observe the outcome of the combination of the different features, but you don’t know all the details about how each feature impacts the target.

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Once you determine the value for each coefficient you know the direction, either positive or negative, and the magnitude of the impact each feature has in target.

With a linear model, you’re assuming all features are independent of each other so, for instance, the fact that you got a delivery doesn’t have any impact on how many treats your dog gets in a day.

Additionally, you think there’s a linear relationship between the features and the target.

So, on the days you get to play more with your dog they’ll get more tired and will want to nap for longer. Or, on days when there are no squirrels outside your dog won’t need to nap as much, because they didn’t spend as much energy staying alert and keeping an eye on the squirrels’ every move.

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For how long will your dog nap tomorrow?

With the general idea of the model in your mind, you collected data for a few days. Now you have real observations of the features and the target of your model.

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