‫Hey there.
‫It's Andre GAM 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 floorspace size, and they're just correlated.
‫The prices go up the same every time the land increases and every time the floorspace 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 of forward backwards attribute selection.
‫What does this mean?
‫Well, we can try training our model using different techniques, backward attribute selection.
‫Essentially says train the model on all the attributes and then slowly start taking away attributes
‫or columns to train your model.
‫Does that affect your model?
‫Does it improve the model?
‫Forward attribute selection is the opposite star 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 50th column, all the other attributes
‫that you add 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.