[MUSIC] So how far can we go with this? Well, you can imagine having more complex rules with more conditions in them. And you can also imagine having a whole set of rules and your classifier now becomes checking this whole set of rules to determine the class. So how do we go about building an appropriate set of rules to come up with a classifier? Well one approach is sequential covering, which looks like Like this, you start off with a data set D and you initialize the rule set R to be empty. And you say for each class, say, survived or not survived, you can construct one rule that correctly classifies some instances in D. Right, so you capture some of the people who survived. And does not incorrectly classify any of the other classes. Okay. Okay, and then you add that rule to the rule set, and remove from D all the instances that were correctly classified by this rule. And so we're gonna keep on adding up rules this way. Now that's great, but it doesn't tell you how to actually find that next rule, and so that's what this explains. So, come up with a set with an initial rule R that just trivially assigns a class value to every record. Okay. And so while it incorrectly classifies some of the non-C instances in D, the write r as the antecedent of r determines C. And so, here the antecedent initially is nothing. Okay, and then for each attribute value pair a equals v, where a is one of the attributes and v is some value of that attribute, figure out the accuracy of that rule, just like females predict survival and we determined the accuracy there was 79%. Or male determines Well we're just dealing with one class now, so it's just survival. Fine. And then figure out the attribute value pair that has maximum accuracy over D. Okay, in the case of a tie, choose one that covers more cases. All right, so now update, the rule R with this new condition, A star equals V star. And remove that attribute from consideration in the set A. Okay. And then keep going until you are no longer incorrectly classifying some of the non C attributes instances of D. And now, in the next time around the loop, ant (r) will be the previous condition, and so that's what this notation is just trying to show. So you can have the prefix of the rule and keep adding one more condition. I should say, one more quality condition to the rule one by one. Okay? But the point here is that you're still looking for the Attribute-value pair that has the maximum accuracy. [MUSIC]