Hey there it's Andre game with your quick tip.
That is.
Let's talk about correlation analysis.
What does that mean.
Well correlation analysis simply means which attributes have correlations.
So let's say one column is correlated to another column.
Let's say we're trying to sell our home and there's two columns one column is the size of the land that
the house is on and the other column is the size of the House and the floor space.
Now let's say that when we're analyzing our data we notice that pretty much all houses that have a large
land size also have a large floor space size and they're just correlated the prices go up the same every
time the land increases and every time the floor space increases in this case these attributes have
high correlation with each other.
In this case we can actually remove this from our analysis or from building our model because it might
not affect our model.
This gets to our next point or forward backwards attribute selection.
What does this mean.
Well we can try training our model using different techniques backward attributes selection essentially
says train the model on all the attributes and then slowly to start taking away attributes or columns
to train your model.
It does that affect your model does it improve the model forward attribute selection is the opposite.
Start with just one column when you train the model and keep adding one attribute at a time until you
get the accuracy to plateau.
That is if you keep increasing columns and let's say after the fiftieth column all the other attributes
say you added just don't improve the model.
Well then maybe we might not need it.
This idea of correlation analysis.
The forward backward attribute selection are all ways for us to test our model reduce our data if we
want to and play with our model instead of just assuming if we include everything everything will make
the model better.
That's often not usually the case.
All right.
Back to Daniel.