I am working on binary classification of data and I want to know the advantages and disadvantages of using Support vector machine over decision trees and Adaptive Boosting algorithms.
Advantages of SVM over decion trees and AdaBoost algorithm
8.2k Views Asked by Akshay Kekre At
1
There are 1 best solutions below
Related Questions in MACHINE-LEARNING
- Bot listening to another bot in group conversation
- How can I add a configuration page for my slack app?
- Slack-Winston in Node not working
- Detect the speaker of Google Home or Amazon's Alexa
- Auto-add a bot to all channels on Slack?
- How to set Slack reminder for a range of dates
- kapacitor: setting slack webhook URL in .tick file
- Notification on slack username change
- Private Message to User via Web API, not RTM API on Slack
- How to send attachment in slack bot using SlackClient for node.js
Related Questions in CLASSIFICATION
- Bot listening to another bot in group conversation
- How can I add a configuration page for my slack app?
- Slack-Winston in Node not working
- Detect the speaker of Google Home or Amazon's Alexa
- Auto-add a bot to all channels on Slack?
- How to set Slack reminder for a range of dates
- kapacitor: setting slack webhook URL in .tick file
- Notification on slack username change
- Private Message to User via Web API, not RTM API on Slack
- How to send attachment in slack bot using SlackClient for node.js
Related Questions in SVM
- Bot listening to another bot in group conversation
- How can I add a configuration page for my slack app?
- Slack-Winston in Node not working
- Detect the speaker of Google Home or Amazon's Alexa
- Auto-add a bot to all channels on Slack?
- How to set Slack reminder for a range of dates
- kapacitor: setting slack webhook URL in .tick file
- Notification on slack username change
- Private Message to User via Web API, not RTM API on Slack
- How to send attachment in slack bot using SlackClient for node.js
Related Questions in DECISION-TREE
- Bot listening to another bot in group conversation
- How can I add a configuration page for my slack app?
- Slack-Winston in Node not working
- Detect the speaker of Google Home or Amazon's Alexa
- Auto-add a bot to all channels on Slack?
- How to set Slack reminder for a range of dates
- kapacitor: setting slack webhook URL in .tick file
- Notification on slack username change
- Private Message to User via Web API, not RTM API on Slack
- How to send attachment in slack bot using SlackClient for node.js
Related Questions in ADABOOST
- Bot listening to another bot in group conversation
- How can I add a configuration page for my slack app?
- Slack-Winston in Node not working
- Detect the speaker of Google Home or Amazon's Alexa
- Auto-add a bot to all channels on Slack?
- How to set Slack reminder for a range of dates
- kapacitor: setting slack webhook URL in .tick file
- Notification on slack username change
- Private Message to User via Web API, not RTM API on Slack
- How to send attachment in slack bot using SlackClient for node.js
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
Popular # Hahtags
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Something you might want to do is use weka, which is a nice package that you can use to plug in your data and then try out a bunch of different machine learning classifiers to see how each works on your particular set. It's a well-tread path for people who do machine learning.
Knowing nothing about your particular data, or the classification problem you are trying to solve, I can't really go beyond just telling you random things I know about each method. That said, here's a brain dump and links to some useful machine learning slides.
Adaptive Boosting uses a committee of weak base classifiers to vote on the class assignment of a sample point. The base classifiers can be decision stumps, decision trees, SVMs, etc.. It takes an iterative approach. On each iteration - if the committee is in agreement and correct about the class assignment for a particular sample, then it becomes down weighted (less important to get right on the next iteration), and if the committee is not in agreement, then it becomes up weighted (more important to classify right on the next iteration). Adaboost is known for having good generalization (not overfitting).
SVMs are a useful first-try. Additionally, you can use different kernels with SVMs and get not just linear decision boundaries but more funkily-shaped ones. And if you put L1-regularization on it (slack variables) then you can not only prevent overfitting, but also, you can classify data that isn't separable.
Decision trees are useful because of their interpretability by just about anyone. They are easy to use. Using trees also means that you can also get some idea of how important a particular feature was for making that tree. Something you might want to check out is additive trees (like MART).