Category: Isye 6501 final

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Bfor regulations regarding readmission. Please see the official Grade Substitution Policy for details. A very limited Grade Substitution Policy exists for first time freshman who receive a D or an F in a course within their first two terms in residence.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

This course introduced fundamental machine learning models used in analytics, cross-cutting concepts, combining models, and software. There were two major objectives, given a business or other question, selecting an appropriate analytics model to answer it, specify the data we will need, and understand what the model's solution will and will not provide as an answer and given someone else's use of analytics to address a specific business or other question, evaluate whether they have used an appropriate model and appropriate data and whether their conclusion is reasonable.

This course included ten assignments, two midterms, a course project, and a final exam. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. ISYE Analytics Modeling This course introduced fundamental machine learning models used in analytics, cross-cutting concepts, combining models, and software. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window.He made important contributions to the supply chain field, particularly in warehousing and logistics.

ISyE Professor Pinar Keskinocak and her team have developed a model that better matches flu vaccine supply with actual regional demand. The H. Milton Stewart School of Industrial and Systems Engineering ISyE has achieved national and international prominence through its tradition of unparalleled excellence and leadership in research, education, and service.

This distinction is due to ISyE's world-class faculty, top-notch students, outstanding curricula, and extensive research focusing on improving quality of life.

isye 6501 final

The quality and versatility of an industrial engineering degree from Georgia Tech's Stewart School of Industrial and Systems Engineering makes it one of the best engineering degrees available.

Working at the intersection of engineering, mathematics, computing, and business, students learn to design the systems behind any number of products and services that touch your life every day. Take advantage of the myriad ways that ISyE supports you to succeed and make the most of your time at Georgia Tech. ISyE alumni are engaged leaders who can be found around the globe in leadership positions within academia, consulting, engineering, financial services, healthcare, law, manufacturing, warehousing, retailing, transportation, and more.

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This course introduced fundamental machine learning models used in analytics, cross-cutting concepts, combining models, and software. There were two major objectives, given a business or other question, selecting an appropriate analytics model to answer it, specify the data we will need, and understand what the model's solution will and will not provide as an answer and given someone else's use of analytics to address a specific business or other question, evaluate whether they have used an appropriate model and appropriate data and whether their conclusion is reasonable.

This course included ten assignments, two midterms, a course project, and a final exam. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. ISYE Analytics Modeling: This course introduced fundamental models used in analytics, cross-cutting concepts, combining models, and software.

Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit b May 28, ISYE Analytics Modeling This course introduced fundamental machine learning models used in analytics, cross-cutting concepts, combining models, and software.

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Simulation with Arena - 1

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ISyE 6669: Deterministic Optimization

Course Project. CP rev4 - Final. Apr 17, Using the same crime data set uscrime. In R, you can use the tree package or the rpart package, and the randomForest package. For each model, describe one or two qualitative takeaways you get from analyzing the results i. After trying some different parameters I determined that the standard minimum amount of calues in a node to split was twenty. Given our small sample size, this will generate a shallow tree.

After using rpart we find that there were only three splits created when regressing on our small sample. You can see in the above output that the lowest cross-validation error is on our model with three splits. We can prune our tree, or in this case select the deepest model to obtain our optimal tree. We consider this to be our best tree based on its cross-validation error not the relative error.

It's important to prune our model, especially if we allow many splits, to avoid overfitting the data. Thus, our final model is the following:. Origonally, the largest variance amount outputs was among NWPo1and Pop.

After pruning, we only use Po1 and NW.

'+_.J(b)+"

These appear to be the only features required to make a decent tree. Then again for NW at 7. We can visualize this sectioning with the following figure:.

isye 6501 final

If we use this model to predict we'll end up with only three possible outcomes. This may seem weird but if we were to have a perfect descision tree all the samples falling into one leaf would be flat or the same value. Thus, our lowest error comes from prediction all points to be one of three values four if we don't have complete information and have to average.

The prediction vs. Based on the 47 points we used to build the model.

isye 6501 final

Notice how the distribution in the middle is more compact or slim the the extrema. This is similar for OLS or other linear models. One way to view Creating a random forest is just creating many decision trees to average out the randomness.

My thought process for growing trees was to grow 10x the number of points we can expect all points will be used multiple times while still not wasting time recomputing trees. We expect tress to be shallow so we'll most likely have many repeats. Random forest models can be computationally expensive and saving time growing trees can greatly reduce the number time to compute. That's why I don't use or more trees. In addition, I didn't want to use more than features since the tree I built in part 1 only had three features.

This is obviously to avoid overfitting the data. If we build more than trees, we start to see the error plataeu out. This is because we run out of unique trees we're able to build. Occam's razor tells us to simplify it down to fewer trees and avoid the redundancy. The random forest averages the results of the many trees which are easy to get predictions from after they are built since they are just a lookup.


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