I am not able to understand the optional lab that implements Linear Regression using Scikit Learn. I want to learn the Scikit learn library. Would help if good resources on scikit and matplotlib are shared?

Hi there,

In general, the scikit-learn documentation is quite good. Feel free to take a look at these examples and the underlying documentation:

- Linear Regression Example β scikit-learn 1.1.2 documentation
- Principal Component Regression vs Partial Least Squares Regression β scikit-learn 1.1.2 documentation

Can you outline what specifically is not clear?

Best regards

Christian

also, what do those values inside fit function do? Fit method takes training data set in its argument,right? but in the picture, I donβt quite get it?

Hi!

The values inside the fit function are a list of data points and labels.

The data is an array of arrays containing 2 values, and the labels are a single array with values representing the labels for the corresponding rows in the first array.

Regarding the picture:

You can see that the linear function was fitted to the training data, meaning the cost function (least squares sum) is minimised, meaning the function coefficients get parametrised resp. calibrated:

- gradient of the linear function
- y intercept

so that the function suits all training observations with feature x and label y in an optimal way.

In general x does not need to be 1D but the linear model can be dependent on more dimensional Features of course, e.g describing a plane y = f(x_1,x_2) etc.

Check out the description here:

Best

Christian