I am curious about the interpretation of the elastic Net coefficient

I want to discover the importance of variables in data through sklearn's Elactic Net. But I don't understand the exact meaning of coefficient. When training, I used alpha: 0.01585598, l1_ratio: 1.000.

The graph below is a coefficient graph drawn from my data. My goal is to predict Time Spend through various variables. Please understand that the column names are marked with A, B, C, and D due to personal information.

In the graph, what does variable A mean for coefficients greater than 150? Do I need to understand that using variable A takes more than 150 seconds when predicting Time Spend? Or does it just mean that variable A has a high positive correlation with the value of y? I drew a coefficient graph through learning, but I am asking a question to understand the meaning accurately.

Thanks for reading this far!

Topic elastic-net scikit-learn python

Category Data Science

1
answered at

A linear model is in the form of:

$y = a\cdot x_1 + b\cdot x_2 + c\cdot x_3.....$

Where $x_n$ is the feature and the letters a,b,c, is the coefficient.

In your figure you are plotting the coefficient a,b,c...

Lets say that you coefficients are $a=1$, $b=2$ and $c=10$

If you feature are $x_1=0$, $x_2=10$ and $x_3 = 1$

then your prediction will be $y = 1 \cdot 0 + 2 \cdot 10 + 10 \cdot 1 = 30$

what does variable "A" mean for coefficients greater than 150? Nothing special, its just a multiplication.

Do I need to understand that using variable "A" takes more than 150 seconds when predicting "Time Spend"? It has nothing to do with the computational time of the algorithm. It has to do with the meaning of the feature.

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