## Cannot understand with sklearn's PolynomialFeatures

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sklearn polynomial regression
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Need help in sklearn's Polynomial Features. It works quite well with one feature but whenever I add multiple features, it also outputs some values in the array besides the values raised to the power of the degrees. For ex: For this array,

```X=np.array([[230.1,37.8,69.2]])
```

when I try to

```X_poly=poly.fit_transform(X)
```

It outputs

```[[ 1.00000000e+00 2.30100000e+02 3.78000000e+01 6.92000000e+01
5.29460100e+04 8.69778000e+03 1.59229200e+04 1.42884000e+03
2.61576000e+03 4.78864000e+03]]
```

Here, what is `8.69778000e+03,1.59229200e+04,2.61576000e+03` ?

If you have features `[a, b, c]` the default polynomial features(in `sklearn` the degree is 2) should be `[1, a, b, c, a^2, b^2, c^2, ab, bc, ca]`.

`2.61576000e+03` is `37.8x62.2=2615,76` (`2615,76 = 2.61576000 x 10^3`)

In a simple way with the `PolynomialFeatures` you can create new features. There is a good reference here. Of course there are and disadvantages("Overfitting") of using `PolynomialFeatures`(see here).

Edit: We have to be careful when using the polynomial features. The formula for calculating the number of the polynomial features is `N(n,d)=C(n+d,d)` where `n` is the number of the features, `d` is the degree of the polynomial, `C` is binomial coefficient(combination). In our case the number is `C(3+2,2)=5!/(5-2)!2!=10` but when the number of features or the degree is height the polynomial features becomes too many. For example:

```N(100,2)=5151
N(100,5)=96560646
```

So in this case you may need to apply regularization to penalize some of the weights. It is quite possible that the algorithm will start to suffer from curse of dimensionality (here is also a very nice discussion).

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PolynomialFeatures generates a new matrix with all polynomial combinations of features with given degree.

Like [a] will be converted into [1,a,a^2] for degree 2.

You can visualize input being transformed into matrix generated by PolynomialFeatures.

```from sklearn.preprocessing import PolynomialFeatures
a = np.array([1,2,3,4,5])
a = a[:,np.newaxis]
poly = PolynomialFeatures(degree=2)
a_poly = poly.fit_transform(a)
print(a_poly)
```

Output:

``` [[ 1.  1.  1.]
[ 1.  2.  4.]
[ 1.  3.  9.]
[ 1.  4. 16.]
[ 1.  5. 25.]]
```

You can see matrix generated in form of [1,a,a^2]

To observe polynomial features on scatter plot, let's use number 1-100.

```import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PolynomialFeatures

#Making 1-100 numbers
a = np.arange(1,100,1)
a = a[:,np.newaxis]

#Scaling data with 0 mean and 1 standard Deviation, so it can be observed easily
scaler = StandardScaler()
a = scaler.fit_transform(a)

#Applying PolynomialFeatures
poly = PolynomialFeatures(degree=2)
a_poly = poly.fit_transform(a)

#Flattening Polynomial feature matrix (Creating 1D array), so it can be plotted.
a_poly = a_poly.flatten()
#Creating array of size a_poly with number series. (For plotting)
xarr = np.arange(1,a_poly.size+1,1)

#Plotting
plt.scatter(xarr,a_poly)
plt.title("Degree 2 Polynomial")
plt.show()
```

Output: Changing degree=3 ,we get: [PDF] scikit-learn user guide, If your problem raises an exception that you do not understand (even after googling it), please make signals that traditional tools cannot see. Typically, any structured dataset includes multiple columns – a combination of numerical as well as categorical variables. A machine can only understand the numbers. It cannot understand the text. That’s essentially the case with Machine Learning algorithms too.

You have 3-dimensional data and the following code generates all poly features of degree 2:

```X=np.array([[230.1,37.8,69.2]])
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures()
X_poly=poly.fit_transform(X)
X_poly
#array([[  1.00000000e+00,   2.30100000e+02,   3.78000000e+01,
#      6.92000000e+01,   5.29460100e+04,   8.69778000e+03,
#      1.59229200e+04,   1.42884000e+03,   2.61576000e+03,
#      4.78864000e+03]])
```

This can also be generated with the following code:

```a, b, c = 230.1, 37.8, 69.2 # 3-dimensional data
np.array([[1,a,b,c,a**2,a*b,c*a,b**2,b*c,c**2]]) # all possible degree-2 polynomial features
# array([[  1.00000000e+00,   2.30100000e+02,   3.78000000e+01,
6.92000000e+01,   5.29460100e+04,   8.69778000e+03,
1.59229200e+04,   1.42884000e+03,   2.61576000e+03,
4.78864000e+03]])
```

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• @TechieBoy101, The default `PolynomialFeatures` in `sklearn` includes all polynomial combinations. You can add `interaction_only=True` to exclude the powers like `a^2, b^2, c^2`. Of course you can exclude the interaction if your model performs better - the `PolynomialFeatures` are a simple way to derive new features (in some artificial manner).