How and when to use polynomial regression?
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# How and when to use polynomial regression?

This recipe explains how and when to use polynomial regression

0

## Recipe Objective

Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial.

So this recipe is a short example on How and when to use polynomial regression. Let's get started.

## Step 1 - Import the library

``` from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures ```

Let's pause and look at these imports. We have exported train_test_split which helps in randomly breaking the datset in two parts. Here sklearn.dataset is used to import one classification based model dataset. Also, we have exported LinearRegression and PolynomialFeatures to build the model.

## Step 2 - Setup the Data

``` X,y=load_boston(return_X_y=True) poly = PolynomialFeatures(degree = 2) X = poly.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25) ```

Here, we have used load_boston function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Thereby, we have introduced polnomial features in our dataset for degree upto 2. Further with have broken down the dataset into 2 parts, train and test with ratio 3:4.

Now our dataset is ready.

## Step 3 - Building the model

``` model = LinearRegression() ```

We have simply built a regressor model with LinearRegression (our data already has polnomial features and linear regreession simply means predicting coefficient) with default values.

## Step 4 - Fit the model and predict for test set

``` model.fit(X_train, y_train) expected_y = y_test predicted_y = model.predict(X_test) ```

Here we have simply fit used fit function to fit our model on X_train and y_train. Now, we are predicting the values of X_test using our built model.

## Step 5 - Printing the results

``` print(model.score(X_train,y_train)) print(model.score(X_test,y_test)) ```

Here we have calculating accuracy score of our trained set and also, on the unknown dataset (X_test and y_test)

## Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

```0.9371457121192392
0.7328994385490928
```

The model has low accuracy score on unknown datset and hence might not be that efficient.

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