All language subtitles for 08 - Feature Combination and Dimensionality Reduction.en

[Autogenerated] in this clip, Let's first

more to discussing feature combination.

Now it's quite possible that the raw data

that you're working with contains very

granular information, which doesn't have

much predictive power. Feature combination

may involve aggregating and bringing

features together to get a future with

more political power. Now you might find

it in the real bold. Some features

naturally work better when they're

considered together. Ah, feature by itself

may not contain much information, but then

considered in conjunction with another

feature. The teachers and combination

might contain information that is relevant

to your model. It's quite possible that

the original feature might be to row or

two granular. Bringing features together

can help improve the predictive power of

features. Let's say you're building a

machine learning model tow. Predict

traffic patterns in a city. Let's say the

city is a bang lord. Now you might get

information from the day off the beat that

it is. You might also get information from

the time off the But when taken in

conjunction, when you use a future cross

day off the week plus time off the day,

you might get a resulting feature that has

more predictive power If you're looking at

traffic at Friday evening at 60 m, you

know it's going to be terrible. But if

you're looking at the same time, 60 M on a

Sunday traffic is quite likely not as bad.

Let's say you want to combine features

together to predict temperature. You can

take into account the current season

whether it's spring, summer, fall or

winter. You can also take into account the

time off the but when taken together, you

might find that the feature combination is

more than the sum of the parts. And

finally, let's move on to the last

component that we'll discuss with in

future engineering. At this dimensionality

reduction. When you're working with data

in the real world, you will find that a

common problem toe have is that you have

too much data. This is a curse and not a

blessing, and it's often referred to as

the cost of dimensionality. This is where

he would apply pre processing algorithms

to reduce the complexity off raw features,

and the specific aim off these algorithms

is to reduce the number of input features

so you have fewer features to work with

having too many features to work within

your data is referred to us the cost of

dimensionality, and it leads to several

problems. You have problems visualizing

your data. You encounter problems during

training as Celeste hearing prediction.

When you work with higher dimensionality

data machine learning models find it hard

to find patterns within your data leading

toe poor quality models over fitted

models. Forfeited models are those that

perform well in training but poorly in the

real world. In production, dimensionality

reduction explicitly aims to solve the

coast of dimensionality while preserving

as much information as possible from the

underlying features. You don't want to

lose too much information. Dimensionality

reduction is a form of unsupervised

learning. You're working with an unlabeled

corpus of data based on the kind of data

that you're working with. There are many

different techniques that you can use for

dimensionality reduction. When you're

working with linear data, you can choose

principle components analysis, which

involves Lee orienting your original data

so that their projected along newer,

better axes. If you're working with non

linear data, you can apply manifold

learning techniques. This involved

unrolling complex forms off Dayton and

higher access toe express data in a

simpler form with lower dimensionality.

Manafort Island techniques are similar toe

unrolling a carpet so that it's

represented in two dimensions. Leading

semantic analysis is a topic modeling and

dimensionality reduction technique that

you can use to work with text data if

you're working with images auto, including

confined, efficient, lower dimensionality representation for your images.