Scenario - I have data that does not have labels but I can create a function to label the data based on behavior and deploy the model so I don't have to keep labeling the data. Is this considered machine learning?
Objective: classify accounts with Volume spikes based on high medium low labels to deploy on big data (trillions of lines of data)
Data: the data I have includes the following attributes:
Account, Time, Date, Volume amount.
Method:
Create a new feature column called "spike" and create a pandas function to ID a spike greater than 5. Is this feature engineering?
Next I create my label column and classify it as low medium or high spike.
Next I Train a machine learning classifier and deploy it to label future accounts with similar patterns in big data.
Thoughts on this process? Is this approach correct for Machine learning?
1st question:
If your algorithm takes the decision, that is, put a label in a sample, based on the set of samples that you have, I'd say it's a machine learning algorithm. But if you design a code that takes into account your experience regarding the data, I think it's not an ML method. In brief, ML look at the data to get patterns and insights from them. I don't know why you're doing that, but is it need to be an ML algorithm? Sometimes you can solve the problem in a very simple way, without using ML.
2nd question: I'm afraid not. Select your data attributes (ex: Account, Time, Date, Volume amount), checking their correlations, try to figure out if you have a dominant one, etc. This process is pre ML. The feature engineering will select what are the best features to present to our algorithm in order to perform the classification (in your case)
3rd question: I think it's fair enough to start playing with some ML algorithms, such as KNN, SVM, NNs, Decision Tree, etc.
Related
I am hoping to have some low code model using Azure AutoML, which is really just going to the AutoML tab, running a classification experiment with my dataset, after it's done, I deploy the best selected model.
The model kinda works (meaning, I publish the endpoint and then I do some manual validation, seems accurate), however, I am not confident enough, because when I am looking at the explanation, I can see something like this:
4 top features are not really closely important. The most "important" one is really not the one I prefer it to use. I am hoping it will use the Title feature more.
Is there such a thing I can adjust the importance of individual features, like ranking all features before it starts the experiment?
I would love to do more reading, but I only found this:
Increase feature importance
The only answer seems to be about how to measure if a feature is important.
Hence, does it mean, if I want to customize the experiment, such as selecting which features to "focus", I should learn how to use the "designer" part in Azure ML? Or is it something I can't do, even with the designer. I guess my confusion is, with ML being such a big topic, I am looking for a direction of learning, in this case of what I am having, so I can improve my current model.
Here is link to the document for feature customization.
Using the SDK you can specify "feauturization": 'auto' / 'off' / 'FeaturizationConfig' in your AutoMLConfig object. Learn more about enabling featurization.
Automated ML tries out different ML models that have different settings which control for overfitting. Automated ML will pick which overfitting parameter configuration is best based on the best score (e.g. accuracy) it gets from hold-out data. The kind of overfitting settings these models has includes:
Explicitly penalizing overly-complex models in the loss function that the ML model is optimizing
Limiting model complexity before training, for example by limiting the size of trees in an ensemble tree learning model (e.g. gradient boosting trees or random forest)
https://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls
I have a problem whereby our users receive the balance of an account each day, and based on the balance, perform an action.
Given the list of historical balances and resulting actions, is it possible to use machine learning to predict the future actions? Preferably in the .net platform.
Thanks.
Ark
I've never used .NET for any data analytics, but I'm sure it won't be too terribly difficult to transpose what I say here into logic in .NET
One of the things people don't like about data sciences is that in order to see if something IS actually possible (predicting future outcomes in this case), you need to do a lot of exploring with the data and see if the data has enough of a pattern to be learned (by either human or by a ML algorithm).
The way to do this would be to shuffle and split the data in some way...let's say into one group with 70 percent of the data and a second with 30 percent of the data.
Once you do this, you want to train some algorithm with the first group (training set) and use the second group(test set) to verify the accuracy of your algorithm.
So how do you chose an algorithm? That's the trickiest part. Only you can say which is best for your particular scenario given full access to the data. However, given that your output seems to be very discrete (let's say max 5 actions), that makes this a supervised learning classification problem. I'd do some analysis using one of these algorithms (SVM, kNN, and DecisionsTrees are a few popular ones), and use some error LIKE F1 or R^2 to determine how well your fitted algorithm performs on your test set.
To perform supervised Machine Learning in .NET, the ML.NET Framework has been announced, and a preview is now available (as of 7th May 2018).
A good starting place for ML.NET is here.
I'm curious as to whether research been done into random forests that combine unsupervised with supervised learning in a way allowing a single algorithm to find patterns in, and work with, multiple different data sets. I have googled every possible way to find research on this, and have come up empty. Can anyone point me in the right direction?
Note: I have already asked this question in the Data Sciences forum, but it's basically a dead forum so I came here.
(also read the comments and will incorporate the content in my answer)
From what I read between the lines is that you want to use Deep networks in a transfer learning setting. However, this would not be based on decision trees.
http://jmlr.csail.mit.edu/proceedings/papers/v27/mesnil12a/mesnil12a.pdf
There are many elements in your question:
1.) Machine learning algorithms, in general, don't care about the source of your data set. So basically you can feed the learning algorithms 20 different data sets and it will use all of them. However, the data should have the same underlying concept (except in the transfer learning case see below). This means: if you combine cats/dogs data with bills data this will not work or make it much harder for the algorithms. At least all input features need to be identical (exceptions exists), e.g, it is hard to combine images with text.
2.) labeled/unlabeled: Two important terms: a data set is a set of data points with a fixed number of dimensions. Datapoint i might be described as {Xi1,....Xin} where each Xi might for example be a pixel. A label Yi is from another domain, e.g., cats and dogs
3.) unsupervised learning data without any labels. (I have the gut feeling that this is not what you want.
4.) semi-supervised learning: The idea is basically that you combine data where you have labels with data without labels. Basically you have a set of images labeled as cats and dogs {Xi1,..,Xin,Yi} and a second set which contains images with cats/dogs but no labels {Xj1,..,Xjn}. The algorithm can use this information to build better classifiers as the unlabeld data provide information on how images look in general.
3.) transfer learning (I think this come closest to what you want). The Idea is that you provide a data set of cats and dogs and learn a classifier. Afterwards you want to train the classifier with images of cats/dogs/hamster. The training does not need to start from scratch but can use the cats/dogs classifier to converge much faster
4.) feature generation / feature construction The idea is that the algoritm learns features like "eyes". This features are used in the next step to learn the classifier. I'm mainly aware of this in the context of deep learning. Where the algoritm learns in the first step concepts like edges and constructs increasingly complex features like faces cats intolerant it can describe things like "the man on the elephant. This combined with transfer learning is probably what you want. However deep learning is based on Neural networks besides a few exceptions.
5.) outlier detection you provide a data set of cats/dogs as known images. When you provide the cats/dogs/hamster classifier. The classifier tells you that it has never seen something like a hamster before.
6.) active learning The idea is that you don't provide labels for all examples (Data points) beforehand, but that the algorithms asks you to label certain data points. This way you need to label much less data.
This is my problem description:
"According to the Survey on Household Income and Wealth, we need to find out the top 10% households with the most income and expenditures. However, we know that these collected data is not reliable due to many misstatements. Despite these misstatements, we have some features in the dataset which are certainly reliable. But these certain features are just a little part of information for each household wealth."
Unreliable data means that households tell lies to government. These households misstate their income and wealth in order to unfairly get more governmental services. Therefore, these fraudulent statements in original data will lead to incorrect results and patterns.
Now, I have below questions:
How should we deal with unreliable data in data science?
Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
-How can we evaluate our errors in this study? Since we have unlabeled dataset, should I look for labeling techniques? Or, should I use unsupervised methods? Or, should I work with semi-supervised learning methods?
Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
Please introduce me any ideas or references which can help me in this issue.
Thanks in advance.
Q: How should we deal with unreliable data in data science
A: Use feature engineering to fix unreliable data (make some transformations on unreliable data to make it reliable) or drop them out completely - bad features could significantly decrease the quality of the model
Q: Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
A: ML algorithms are not magic sticks, they can't figure out anything unless you tell them what you are looking for. Can you describe what means 'unreliable'? If yes, you can, as I mentioned, use feature engineering or write a code which will fix the data. Otherwise no ML algorithm will be able to help you, without the description of what exactly you want to achieve
Q: Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
A: I don't think so just because the question itself is too open-ended. What means 'the quality of the data'?
Generally, here are couple of things for you to consider:
1) Spend some time on googling feature engineering guides. They cover how to prepare your data for you ML algorithms, refine it, fix it. Good data with good features dramatically increase the results.
2) You don't need to use all of features from original data. Some of features of original dataset are meaningless and you don't need to use them. Try to run gradient boosting machine or random forest classifier from scikit-learn on your dataset to perform classification (or regression, if you do regression). These algorithms also evaluate importance of each feature of original dataset. Part of your features will have extremely low importance for classification, so you may wish to drop them out completely or try to combine unimportant features together somehow to produce something more important.
I am trying to solve some classification problem. It seems many classical approaches follow a similar paradigm. That is, train a model with some training set and than use it to predict the class labels for new instances.
I am wondering if it is possible to introduce some feedback mechanism into the paradigm. In control theory, introducing a feedback loop is an effective way to improve system performance.
Currently a straight forward approach on my mind is, first we start with a initial set of instances and train a model with them. Then each time the model makes a wrong prediction, we add the wrong instance into the training set. This is different from blindly enlarge the training set because it is more targeting. This can be seen as some kind of negative feedback in the language of control theory.
Is there any research going on with the feedback approach? Could anyone shed some light?
There are two areas of research that spring to mind.
The first is Reinforcement Learning. This is an online learning paradigm that allows you to get feedback and update your policy (in this instance, your classifier) as you observe the results.
The second is active learning, where the classifier gets to select examples from a pool of unclassified examples to get labelled. The key is to have the classifier choose the examples for labelling which best improve its accuracy by choosing difficult examples under the current classifier hypothesis.
I have used such feedback for every machine-learning project I worked on. It allows to train on less data (thus training is faster) than by selecting data randomly. The model accuracy is also improved faster than by using randomly selected training data. I'm working on image processing (computer vision) data so one other type of selection I'm doing is to add clustered false (wrong) data instead of adding every single false data. This is because I assume I will always have some fails, so my definition for positive data is when it is clustered in the same area of the image.
I saw this paper some time ago, which seems to be what you are looking for.
They are basically modeling classification problems as Markov decision processes and solving using the ACLA algorithm. The paper is much more detailed than what I could write here, but ultimately they are getting results that outperform the multilayer perceptron, so this looks like a pretty efficient method.