How to do we implement polynomial regression?

Does it use this code part from the lab:

**lin_model(degree)**

or does it use

PolynomialFeatures

Hello @Adveat_Prasad_Karnik

I think you are talking about the Course 2 Week 3 assignment.

There are two independent steps:

## 1. model

```
degree = 10
lmodel = lin_model(degree)
lmodel.fit(X_train, y_train)
```

`lin_model`

instantiate a model, and `lmodel.fit`

trains the model with the data `(X_train, y_train)`

.

## 2. data

If your data has polynomial features, then the model is a polynomial regression model. To generate polynomial features, we can use, as you said, the `PolynomialFeatures`

provided by sklearn.

## Summary

So, if you apply `PolynomialFeatures`

to your raw data, you can call the model learnt with that data a polynomial regression model.

Raymond

1 Like

I see.

Thank you so much for making things clearer for me!