 of patients, during surgery (5.7 %) or thereafter (7.7 %). Overall survival to discharge was 79.5 %. Most frequent complications were stroke and paralysis (15.2 %), but only visceral malperfusion (OR 9.0) and heart failure mandating ECMO therapy (OR 29.5) were associated with significantly increased mortality.
CONCLUSIONS: Surgery for type A aortic dissection is still challenging. Along with the refinement of surgical techniques, the indication for the various procedures has moved from a simplified general strategy to a more individualized concept.Which machine learning algorithm(s) can be used for my specific problem?
This is a fundamental question we should always ask and find the answer to before using any machine learning algorithm.
This blog post will share what factors should be considered and the questions that need to be answered in order to select the most appropriate machine learning algorithm(s) to solve the problem at hand.
Machine learning algorithms are divided into three types: supervised, unsupervised and reinforcement learning.
Question one: what type of machine learning algorithm should I select?
- 1. Supervised learning: we have labelled datasets, and the goal is to predict the future outcomes.
- 2. Unsupervised learning: data points have no labels associated with them, and the goal is to organize the data in some way or discover the intrinsic patterns that underlie the data.
- 3. Reinforcement learning: the algorithm gets to choose an action in response to each data point with the goal of achieving the highest reward based on how good the decision of the algorithm was.
-
After deciding if the problem is supervised, unsupervised or reinforcement learning we need to look at the available algorithms under each of these categories. Here are some examples of supervised and unsupervised algorithms:
Supervised:
- Classification for predicting a categorical variable:
- o Linear SVM
- o Naive Bayes
- o Decision Tree
- o Logistic Regression
- o Kernel SVM
- o Random Forest
- o Neural network
- o Gradient Boosting Tree
-
- Regression for predicting a continuous variable:
-
o Linear Regression
o Random Forest
o Neural network
o Gradient Boosting Tree
o Decision Tree
Unsupervised:
- Clustering for grouping data in a way that observations in one group or cluster are more similar (according to some criteria) than those in other groups:
o k-means
o k-modes
o Gaussian Mixture Model
o DBSCAN
- Dimension reduction for reducing the number of variables under consideration, specifically, the redundant or irrelevant features:
o Principal Component Analysis
o Singular Value Decomposition
o Latent Dirichlet Analysis
o Linear Discriminant Analysis (LDA)
o Autoencoders
There is a long list of algorithms available under each type of machine learning algorithms. It is worth noting that there is not one algorithm that works best for every problem since there are many factors at play.
Question two: which algorithm works best for my specific problem?
To determine other factors that should be considered when selecting a machine learning algorithm, the following questions can help narrow your options into a smaller group:
What is the acceptable accuracy level?
Typically, there is a tradeoff between accuracy and training time. If for your case approximation is good enough, you should select algorithms with approximate methods. This helps to save processing time.
What is the available training time?
If you have enough time to spend on training and building the model, then you can move to algorithms that result in higher accuracy.
What is the size of data?
The size of data also affects the accuracy and training time of the algorithms.
What are the assumptions made by the algorithms?
Each algorithm comes with some assumptions. You should ensure you meet the algorithm’s requirements; otherwise, it will reduce the accuracy of your model. These assumptions may be about the number of observations, the relationship between features, the limit on the number of categories, whether features are linear or nonlinear and whether feature values are discrete or continuous, to name a few.
What are the number of parameters?
The more parameters you have for an algorithm, the more time you should spend configuring them and finding the best combination that leads to the highest accuracy.
What are the number of features?
If the number of features is large compared to the number of data points, then only certain algorithms can be used.
What are the memory requirements?
Memory usage of an algorithm is the other factor we should check if we have limited memory available.
What is the result interpretation/explainability of the algorithm?
Some algorithms are more complex than others, therefore it is more difficult to interpret their result. Depending on if you are looking for easy or difficult result interpretation, some algorithms should be selected over others.
Final note: We can use the criteria above to limit the number of algorithms we use to resolve our problem. However, usually more than one algorithm can solve a given problem, and one algorithm may be a better fit than others. So, in cases where it is not possible to know the best algorithm in advance, we can try one of the other candidates, and if the results are not acceptable, we can try the other ones.
In our R video series, we used Decision Tree algorithm to predict the monthly spending on Bike products based on customer features. We used the process above to choose the proper algorithm to solve this problem.
If you’re interested in watching our Data Science Team solve this problem using Microsoft R in SQL Server 2016, sign up for the 6-part video series today.I got separated from my family in nyc in a crowd when I was in middle school, from upstate, got on a subway and realized that I was following the wrong person.    Ended up hanging out with some college guys looking for hookers, they went down to to port authority I think, it was a while ago.   Got some pizza.If you’re one of our regulars, you’ll love this deal.
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Cartographies of the Mind
Philosophy and Psychology in Intersection
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Part of the Studies in Brain and Mind book series (SIBM, volume 4)
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Part of the Studies in Brain and Mind book series (SIBM, volume 4)".Join us this coming Tuesday, March 1st for a special presentation! Starts at 8 pm.
Erin “Robot Grrl” Kennedy is currently a Studio[Y] fellow at the MaRS Discovery District in Toronto. She is an avid robot maker. Her current project is Robot Missions, which aims to enable makers and humanitarians to collaborate on improving the environment with the use of a robot platform. She participated in Fab Academy 2015 where she prototyped unfolding CubeSat robots. She launched a kit named RoboBrrd for people to learn how to build a robot character. Erin is the host of the Robot Party, a Google+ Hangout where robot builders join internationally and show the robots they are working on. She was recognized as an Intel Emerging Young Entrepreneur and won a gold medal in the RoboGames, the robot olympics.
RoboBrrd didn’t start out as the chirpy character it is today. Almost like nature, it evolved through iterations and iterations. I’ll give a history of how RoboBrrd began as a popsicle stick robot, to what it became with a lot of help from the maker community. This involved learning circuit layout, having boards made, learning CAD and laser cutting designs.
Fast forward a bit, and I’ll share the robot creatures that followed (like BotBait), the adventures had through Fab Academy and now what I’m currently working on as a part of Robot Missions while at Studio[Y] and MaRS.If you’re a maker working on a hobby project, but can’t really envision how it could become replicated for 100 other people, this talk will be helpful to you. If you enjoy robotic creatures and seeing ways that digital fabrication can be used, this talk will be enjoyable for you.
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When Mary asked what my topic was for this post, I had a few notes outlined but nothing