Rules: Sequential Covering

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From the course by University of Washington

Practical Predictive Analytics: Models and Methods

282 ratings

University of Washington

Practical Predictive Analytics: Models and Methods

282 ratings

Course 2 of 4 in the Specialization Data Science at Scale

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

From the lesson

Supervised Learning

Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.

Statistics vs. Machine Learning4:16

Simple Examples3:46

Structure of a Machine Learning Problem5:24

Classification with Simple Rules5:01

Learning Rules4:19

Rules: Sequential Covering3:10

Rules Recap2:30

Meet the Instructors

Bill Howe
Director of Research
Scalable Data Analytics

[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]

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