WEKA Tutorial #1.3 - How to Build a Data Mining Model from Scratch

Key Takeaways

in this third and final part of episode 2 we're going to continue where we left off in the two previous episodes where I have shown you from installation of the weakest software to pre-processing the data set and model construction using the decision tree algorithm see four point five in this video I will show you how to interpret the decision rules obtained from the decision tree model so without further ado let's get started so let's have a look at the tree what does it actually look like you can right-click on this label here and then find visualize tree and then this is the tree this is the decision tree created by the j48 or the C four point five algorithm the first one represents the root node and a tango represents the leaf node and so these represents the subsequent branching out of the variables so let's start from the root node here so the first variable is petal width and if the petal width has a value of less than zero minus zero point seven eight four four five seven then we can classify it as being iris setosa and in parentheses 50 of these are using this rule so if the petal width has a value greater than zero point six five six nine one seven then we can say that it is a iris virginica and 46 of these have been correctly classified y1 have been misclassified and so we can do the same with the branching out of know the swells so this means that in order to be classified as iris versicolor here the petal win needs to be in the range of minus 0.78 and 0.65 this is the first variable and the second variable needs to have pedal link value of less than zero six-four to be a iris versicolor and so if we move on to the subsequent branch here the petal length has a value greater and that's 0.64 and the petal width has a value less than 0.39 then we can say that intercity iris virginica however if the petal width has a value of greater than 0.39 then we can see that it is a iris versicolor so this visual tree will allow us to come up or visualize the the if and then rules of the decision tree that have been created and we can see that 96% accuracy was afforded by the tree so very useful and that's about it so congratulations you have just built your first prediction model and in the future videos we're going to cover some more algorithms and other interesting data mining software as well so until next time i'm tellin' Anton cinimon on the data professor channel and if you haven't subscribed yet please consider subscribing and clicking on the notification bell so that you will be notified on the next video so I'll see you in the next one