In my research and learning of Federated Learning, I noticed that most of the researchers are discussing the convergence rates of the models.
Why is that so important and how it can benefit the model discussion?
and if the model fast converged in the first rounds, would that be a bad indication for the model?
Related
I'm doing some research into fraud detection for academic purposes.
I' d like to know specifically about techniques for feature selection\engeneering from a transactional dataset.
In more details, given a dataset of transactions (credit card for example), what kind of features are selected to be used on the model and how are they engineered?
All the papers I've come across focus on the model itself (SVM, NN, ...) not really touching on this subject.
Also, if anyone knows of public datasets that are not anonymized - that would also help.
Thanks
Having a good understanding of feature selection/ranking can be a great asset for a data scientist or machine learning practitioner. A good grasp of these methods leads to better performing models, better understanding of the underlying structure and characteristics of the data and leads to better intuition about the algorithms that underlie many machine learning models.
There are in general two reasons why feature selection is used:
1. Reducing the number of features, to reduce overfitting and improve the generalization of models.
2. To gain a better understanding of the features and their relationship to the response variables.
Possible methods:
Univariate feature selection:
Pearson Correlation
Mutual information and maximal information coefficient (MIC)
Distance correlation
Model based ranking
Tree based methods:
Random forest feature importance (Mean decrease impurity, Mean decrease accuracy)
Others:
stability selection
RFE
In many reinforcement learning (RL) papers, Markov Decision Process (MDP) is a typical problem setting for RL problem. What is the real benefit of this setting? Some papers use LSTM as their policy network structure which obviously violate the MDP assumption and make more sense.
Basically, Markov Decision Processes provide a theoretical framework that allows to analyze the convergence guarantees of the algorithms as well as other theoretical properties. Although LSTM and other deep learning approaches combined with RL have reached impressive results, they lack from a solid theoretical background that allow understand or ensure when the algorithm is going to learn something useful, or how far the learned policy will be from the optimal one.
I have used the extreme learning machine for classification purpose and found that my classification accuracy is only at 70+% which leads me to use the ensemble method by creating more classification model and testing data will be classified based on the majority of the models' classification. However, this method only increase classification accuracy by a small margin. Can I asked what are the other methods which can be used to improve classification accuracy of the 2 dimension linearly inseparable dataset ?
Your question is very broad ... There's no way to help you properly without knowing the real problem you are treating. But, some methods to enhance a classification accuracy, talking generally, are:
1 - Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution. Then you will know what data is better to train a more accurate model.
2 - Cross Dataset : The same as cross validation, but using different datasets.
3 - Tuning your model : Its basically change the parameters you're using to train your classification model (IDK which classification algorithm you're using so its hard to help more).
4 - Improve, or use (if you're not using) the normalization process : Discover which techniques (change the geometry, colors etc) will provide a more concise data to you to use on the training.
5 - Understand more the problem you're treating... Try to implement other methods to solve the same problem. Always there's at least more than one way to solve the same problem. You maybe not using the best approach.
Enhancing a model performance can be challenging at times. I’m sure, a lot of you would agree with me if you’ve found yourself stuck in a similar situation. You try all the strategies and algorithms that you’ve learnt. Yet, you fail at improving the accuracy of your model. You feel helpless and stuck. And, this is where 90% of the data scientists give up. Let’s dig deeper now. Now we’ll check out the proven way to improve the accuracy of a model:
Add more data
Treat missing and Outlier values
Feature Engineering
Feature Selection
Multiple algorithms
Algorithm Tuning
Ensemble methods
Cross Validation
if you feel the information is lacking then this link should you learn, hopefully can help : https://www.analyticsvidhya.com/blog/2015/12/improve-machine-learning-results/
sorry if the information I give is less satisfactory
Basically, my question is, since unsupervised learning is a type of machine learning, does there need to be some aspect of the machine "learning" and improving based on it's discoveries? For example, if an algorithm is developed that takes unlabeled images and finds associations between them, does it need to improve itself based on those associations to be classified as "unsupervised learning" or is simply reporting those associations good enough to earn that classification?
For example, if an algorithm is developed that takes unlabeled images and finds associations between them...
That is the "learning" in "unsupervised learning," so yes, this would be considered unsupervised learning.
...does it need to improve itself based on those associations...
No, there's no requirement that the algorithm take what it has learned and improves itself to be considered unsupervised learning. Just analyzing the data set and finding previously unknown associations is enough to be considered unsupervised machine learning. The "unsupervised" distinction is really just that the initial data set is unlabeled.
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning algorithms like decision tree, SVM etc. are applicable. My question is, how should I start to approach such problems, I mean whether to start with decision tree or SVM or some other algorithm or is there is any other approach i.e. how will I decide?
So, I had never heard of Kaggle until reading your post--thank you so much, it looks awesome. Upon exploring their site, I found a portion that will guide you well. On the competitions page (click all competitions), you see Digit Recognizer and Facial Keypoints Detection, both of which are competitions, but are there for educational purposes, tutorials are provided (tutorial isn't available for the facial keypoints detection yet, as the competition is in its infancy. In addition to the general forums, competitions have forums also, which I imagine is very helpful.
If you're interesting in the mathematical foundations of machine learning, and are relatively new to it, may I suggest Bayesian Reasoning and Machine Learning. It's no cakewalk, but it's much friendlier than its counterparts, without a loss of rigor.
EDIT:
I found the tutorials page on Kaggle, which seems to be a summary of all of their tutorials. Additionally, scikit-learn, a python library, offers a ton of descriptions/explanations of machine learning algorithms.
This cheatsheet http://peekaboo-vision.blogspot.pt/2013/01/machine-learning-cheat-sheet-for-scikit.html is a good starting point. In my experience using several algorithms at the same time can often give better results, eg logistic regression and svm where the results of each one have a predefined weight. And test, test, test ;)
There is No Free Lunch in data mining. You won't know which methods work best until you try lots of them.
That being said, there is also a trade-off between understandability and accuracy in data mining. Decision Trees and KNN tend to be understandable, but less accurate than SVM or Random Forests. Kaggle looks for high accuracy over understandability.
It also depends on the number of attributes. Some learners can handle many attributes, like SVM, whereas others are slow with many attributes, like neural nets.
You can shrink the number of attributes by using PCA, which has helped in several Kaggle competitions.