WEKA Tutorial #1.2 - How to Build a Data Mining Model from Scratch

Key Takeaways

so let's see to where it is installed okay so it's right here we got 383 so I'll click on the weeka 3.8 okay so I just click on ok here just a warning information telling that we can use many learning schemes and tools and there's a tools menu ok ok and so let's get started and building our model so why don't you click on the Explorer button so in in this weaker Explorer it allows you to intuitively build your prediction model by clicking on specific functions so let's get started by importing the data set and the data set that we're going to use in this practical tutorial will be the famous iris data set iris data set is a public domain data set that is commonly used as an example data set for teaching data mining and let's find it so click on the Open File go to the C Drive go to the Program Files I'm not sure whether it is Program Files here or program files with x86 so let me try the first one so there should be a folder called Rika ok so it's in the Program Files we got 3/8 go to data subfolder and then find iris ok here we go iris dot AR FF now they also have the iris to D so let's go with iris dot AR FF click on open and then this is what we see so let's just have a look at what this the various feature or menus are doing so in this panel here it tells you about the attributes or the variables that this data set has and so we can see that there are a total of five variables the first one being the super lane the super width the pedal length the pit of wit and in a class so this data said it's a data set of 150 flowers that are closely related caught iris and they are described by four variables the length and the width of the sepal the length and the width of the petal and then it is for each flower it is given a corresponding label as being either a C Tosa a versi color or a virginica and so here we can see again that okay this is the iris dataset it has a total of a hundred and fifty flowers and there are five attributes or five variables that we see here and if you click this if you click on it and to the right you will see some description about it so you can see that there are 50 iris setosa 50 iris versicolor 50 iris virginica and there are no missing data here that's good to know so click on the first variable we can see that the minimum value is 4.3 the maximum value is 7.9 the mean value 5.8 for 3 with a standard deviation of 0.8 to 8 and then we get the same information by clicking on the subsequent variables so we see the minimum maximum the mean the standard deviation okay so notice that the numbers here mean and standard deviation it really depends on the average value of each variable and then the standard deviation which is tell us the variability of each variable so before we build a prediction model let's first start by normalizing our data because each variable will have different minimum and mm as you can see the first one has 4.3 a minimum of 4.3 maximum of 7.9 second variable has minimum of 2 maximum 4.4 has the third one has to pedal length of 1 maximum of 6.9 the fourth variable has a minimum of 0.1 maximum of 2.5 and we notice that the mean and standard deviation of each of one of them are different so let's get started by first normal icing or standardizing our variable so let me begin by slicing the minimum and maximum to be 0 and 1 so we can do that very easily in arica so we have to click on the arrow on supervised attributes then click on normalize and then apply so before I click on the button notice that the minimum maximum or zero point one two point five one and six point nine two and 4.4 4.3 and seven point nine so I'll click here apply and all this what change what changes so the minimum value becomes zero and one and we also notice that the me know and also standard deviation also differed and here same thing minimum maximums zero and one third variable same thing zero and one fourth variable zero and one or alternatively so I can undo that alternatively so that will bring us back to the original state so alternatively instead of normalized I could use standardized so it's in the same supporter filter unsupervised attribute and then I'll find the standardized so click on standardize click on apply but notice that the mean and the standard deviation will be altered so we see that they are 5.8 here 0.8 here 3 and 0.4 three point seven one point seven one point one nine nine seven point seven six three so click on apply okay and so the the mean becomes zero and the standard deviation becomes one so this will happen for every one of them dart variable also and also the fourth variable however okay so nothing happens to the class we got that that is the class where we are going to make our classification so in data mining there are many tasks that you can do you could visualize data you could cluster the data you could classify the data you could be at a regression model but for this example because our class or our output label is a qualitative label therefore we will perform classification by classification we mean that we will categorize each of the 150 flowers into one of the three class label here either as a C Tosa versicolor or virginica so this step is called data pre-processing where we normalize or standardize the variable so decide on one of the or the other either normalize or standardize but not both so the one or the other and when you are ready go to the next step which is to click on the classify tab so let's click on that and then go to the classifier so choose a classifier and let's go with a how about a decision tree so let's begin with aj 4.8 so this is essentially using the c 4.5 algorithm by Ross Quinlan so click on the tape 48 and then the default for doing the test would be cross-validation using a 10-fold so I'm going to cover this in a future video about how you can split your data set into training and testing and also how you can do the cross-validation set so in this tutorial I'm going to stick to the default and so we'll click on the start button and then your prediction model will be constructed so you have seen that it takes only a couple of seconds not even a second so maybe half a second to create your model so here this is the summary of your prediction model so let's start by scrolling up on top okay so this provides a description the algorithm that you're using your using T 48 you have a hundred and fifty sample size you have five variables you are using 10-fold cross-validation and this is the resulting decision tree created file inside your prediction model there are total 5 leaves the size of the tree is 9 and then these are the performance metric of your prediction model so you see that you have a 96% accuracy and correctly classifying 144 out of 150 flowers into one of the three classes correctly and that we have six that we have misclassified and we have kappa statistics here in absolute error with me squared error and others as well we also are provided with the true positive rate false positive rate position we call f-measure mcc or the matthews correlation coefficient or i will see the class so here we are given the performance metric for each of the three classes and then this is the average weight of all the three classes and the confusion matrix is provided below so what is the confusion matrix it allows you to see how your prediction model is confused so if you look under the hood you have 50 flowers for each of the iris setosa class 50 flowers for IRS versicolor and 50 flowers for RS virginica so out of 50 we have correctly classified 49 and we have misclassified one of them so a right here 8 is represented by iris setosa and so for the iris setosa out of 50 one of them is misclassified to BB b is iris versus versicolor so we can see that one flower that is supposed to be classified as iris setosa was misclassified as iris versicolor okay so going to the second line we see that 47 iris versicolor have been correctly classified and 3 iris versicolor have been misclassified to be iris virginica okay we'll be gone to the third row 48 Irish virginica have been correctly classified as iris virginica however two of the iris virginica have been misclassified to BB or iris versicolor so this is very useful in helping us to understand the confusion made by our prediction model so until next time I'm telling an x in a mod on the data professor channel and if you haven't subscribed yet please consider subscribing clicking on you know education bell so that you will be modified so I'll see you in [Music]