Original subtitles

The choice of features can have

a huge impact on your learning algorithm's performance.

In fact, for many practical applications,

choosing or entering the right features is

a critical step to making the algorithm work well.

In this video, let's take a look at how you can choose or

engineer the most appropriate features

for your learning algorithm.

Let's take a look at feature engineering by revisiting

the example of predicting the price of a house.

Say you have two features for each house.

X_1 is the width of the lot size

of the plots of land that the house is built on.

This in real state is also

called the frontage of the lot,

and the second feature,

x_2, is the depth of the lot size of,

lets assume the rectangular plot

of land that the house was built on.

Given these two features, x_1 and x_2,

you might build a model like this where f of x

is w_1x_1 plus w_2x_2 plus b,

where x_1 is the frontage or width,

and x_2 is the depth.

This model might work okay.

But here's another option for how

you might choose a different way to

use these features in the model

that could be even more effective.

You might notice that the area of

the land can be calculated

as the frontage or width times the depth.

You may have an intuition that

the area of the land is more predictive of the price,

than the frontage and depth as separate features.

You might define a new feature,

x_3, as x_1 times x_2.

This new feature x_3 is

equal to the area of the plot of land.

With this feature, you can then have a model f_w,

b of x equals w_1x_1 plus w_2x_2

plus w_3x_3 plus b

so that the model can now choose parameters w_1,

w_2, and w_3,

depending on whether the data shows that

the frontage or the depth or the area

x_3 of the lot turns out to be

the most important thing for

predicting the price of the house.

What we just did, creating a new feature

is an example of what's called feature engineering,

in which you might use your knowledge or

intuition about the problem to design new features

usually by transforming or

combining the original features of

the problem in order to make it

easier for the learning algorithm

to make accurate predictions.

Depending on what insights you

may have into the application,

rather than just taking

the features that you happen to have

started off with sometimes by defining new features,

you might be able to get a much better model.

That's feature engineering.

It turns out that this one flavor of feature engineering,

that allow you to fit not just straight lines,

but curves, non-linear functions to your data.

Let's take a look in the next video

at how you can do that.