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 did my price prediction using RandomForest Regressor with Sklearn . I am able to get the feature importance using feature_importances_ function of Randomforest regressor. Now i want to know which features are affecting the individual results of the data that needs to be predicted.
I cannot share the data as it is confidential just think of it as a medical claim file with 8 columns which have correlation with the actual price.
Pardon me if this question sound noob i am new to machine learning world.
Please give me some guidance as i am stuck up.
What you want is called "model interpretation".
There is a famous trade-off between flexibility and interpretability. See here for a short explanation.
Now, Random Forests are fairly flexible, hence are hard to interpret. Some people even claim that this is impossible.
Keeping in mind the above, there are some people who tried to do that.
See here for a way of doing this, and here for a code example.
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 don't know how I should approach this problem:
I have a data set. A user may or may not be part of a funded scheme.
I want to use machine learning to deduce that users that are not part of the scheme were susceptible to certain conditions e.g. 1,2,3 and 4. Those in the scheme were susceptible to 1,2 and 4. Therefore it can be deduced that if you are part of the scheme you won't be susceptible to condition 3.
I have a second related problem as well. Within the funded scheme the user can have two plans (cost different amounts). I would like to see whether those on the cheaper plan were susceptible to more conditions than those on the more expensive plan.
Can anyone help me as to whether this a recommendation or a classification problem and what specific algorithms I should look at?
Thanks.
Neither. It's a statistics problem. Your dataset is complete and you don't mention any need to predict attributes of future subjects or schemes, so training a classifier or a recommender wouldn't seem to serve it's usual goals.
You could use a person's conditions as features and their scheme stats as the target, classify them with SVM and then use the classification performance/accuracy as a measure of the separability of the classes. You could also consider clustering. However, a t-test would do the same thing and is a much more accepted tool to justify the validity of claims like this.
It looks like you are trying to build a system that would classify a user as funded or not funded, and if not funded, reason why they were not funded.
If this is the case, what you need is a machine learning classifier that is interpretable, i.e., the reasoning behind why a classifier makes a certain decision can be conveyed to users. You may want to look at Decisions trees and (to a lesser extent) RandomForest and Gradient Boosted Trees.
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I am wanting some expert guidance here on what the best approach is for me to solve a problem. I have investigated some machine learning, neural networks, and stuff like that. I've investigated weka, some sort of baesian solution.. R.. several different things. I'm not sure how to really proceed, though. Here's my problem.
I have, or will have, a large collection of events.. eventually around 100,000 or so. Each event consists of several (30-50) independent variables, and 1 dependent variable that I care about. Some independent variables are more important than others in determining the dependent variable's value. And, these events are time relevant. Things that occur today are more important than events that occurred 10 years ago.
I'd like to be able to feed some sort of learning engine an event, and have it predict the dependent variable. Then, knowing the real answer for the dependent variable for this event (and all the events that have come along before), I'd like for that to train subsequent guesses.
Once I have an idea of what programming direction to go, I can do the research and figure out how to turn my idea into code. But my background is in parallel programming and not stuff like this, so I'd love to have some suggestions and guidance on this.
Thanks!
Edit: Here's a bit more detail about the problem that I'm trying to solve: It's a pricing problem. Let's say that I'm wanting to predict prices for a random comic book. Price is the only thing I care about. But there are lots of independent variables one could come up with. Is it a Superman comic, or a Hello Kitty comic. How old is it? What's the condition? etc etc. After training for a while, I want to be able to give it information about a comic book I might be considering, and have it give me a reasonable expected value for the comic book. OK. So comic books might be a bogus example. But you get the general idea. So far, from the answers, I'm doing some research on Support vector machines and Naive Bayes. Thanks for all of your help so far.
Sounds like you're a candidate for Support Vector Machines.
Go get libsvm. Read "A practical guide to SVM classification", which they distribute, and is short.
Basically, you're going to take your events, and format them like:
dv1 1:iv1_1 2:iv1_2 3:iv1_3 4:iv1_4 ...
dv2 1:iv2_1 2:iv2_2 3:iv2_3 4:iv2_4 ...
run it through their svm-scale utility, and then use their grid.py script to search for appropriate kernel parameters. The learning algorithm should be able to figure out differing importance of variables, though you might be able to weight things as well. If you think time will be useful, just add time as another independent variable (feature) for the training algorithm to use.
If libsvm can't quite get the accuracy you'd like, consider stepping up to SVMlight. Only ever so slightly harder to deal with, and a lot more options.
Bishop's Pattern Recognition and Machine Learning is probably the first textbook to look to for details on what libsvm and SVMlight are actually doing with your data.
If you have some classified data - a bunch of sample problems paired with their correct answers -, start by training some simple algorithms like K-Nearest-Neighbor and Perceptron and seeing if anything meaningful comes out of it. Don't bother trying to solve it optimally until you know if you can solve it simply or at all.
If you don't have any classified data, or not very much of it, start researching unsupervised learning algorithms.
It sounds like any kind of classifier should work for this problem: find the best class (your dependent variable) for an instance (your events). A simple starting point might be Naive Bayes classification.
This is definitely a machine learning problem. Weka is an excellent choice if you know Java and want a nice GPL lib where all you have to do is select the classifier and write some glue. R is probably not going to cut it for that many instances (events, as you termed it) because it's pretty slow. Furthermore, in R you still need to find or write machine learning libs, though this should be easy given that it's a statistical language.
If you believe that your features (independent variables) are conditionally independent (meaning, independent given the dependent variable), naive Bayes is the perfect classifier, as it is fast, interpretable, accurate and easy to implement. However, with 100,000 instances and only 30-50 features you can likely implement a fairly complex classification scheme that captures a lot of the dependency structure in your data. Your best bet would probably be a support vector machine (SMO in Weka) or a random forest (Yes, it's a silly name, but it helped random forest catch on.) If you want the advantage of easy interpretability of your classifier even at the expense of some accuracy, maybe a straight up J48 decision tree would work. I'd recommend against neural nets, as they're really slow and don't usually work any better in practice than SVMs and random forest.
The book Programming Collective Intelligence has a worked example with source code of a price predictor for laptops which would probably be a good starting point for you.
SVM's are often the best classifier available. It all depends on your problem and your data. For some problems other machine learning algorithms might be better. I have seen problems that neural networks (specifically recurrent neural networks) were better at solving. There is no right answer to this question since it is highly situationally dependent but I agree with dsimcha and Jay that SVM's are the right place to start.
I believe your problem is a regression problem, not a classification problem. The main difference: In classification we are trying to learn the value of a discrete variable, while in regression we are trying to learn the value of a continuous one. The techniques involved may be similar, but the details are different. Linear Regression is what most people try first. There are lots of other regression techniques, if linear regression doesn't do the trick.
You mentioned that you have 30-50 independent variables, and some are more important that the rest. So, assuming that you have historical data (or what we called a training set), you can use PCA (Principal Componenta Analysis) or other dimensionality reduction methods to reduce the number of independent variables. This step is of course optional. Depending on situations, you may get better results by keeping every variables, but add a weight to each one of them based on relevant they are. Here, PCA can help you to compute how "relevant" the variable is.
You also mentioned that events that are occured more recently should be more important. If that's the case, you can weight the recent event higher and the older event lower. Note that the importance of the event doesn't have to grow linearly accoding to time. It may makes more sense if it grow exponentially, so you can play with the numbers here. Or, if you are not lacking of training data, perhaps you can considered dropping off data that are too old.
Like Yuval F said, this does look more like a regression problem rather than a classification problem. Therefore, you can try SVR (Support Vector Regression), which is regression version of SVM (Support Vector Machine).
some other stuff you can try are:
Play around with how you scale the value range of your independent variables. Say, usually [-1...1] or [0...1]. But you can try other ranges to see if they help. Sometimes they do. Most of the time they don't.
If you suspect that there are "hidden" feature vector with a lower dimension, say N << 30 and it's non-linear in nature, you will need non-linear dimensionality reduction. You can read up on kernel PCA or more recently, manifold sculpting.
What you described is a classic classification problem. And in my opinion, why code fresh algorithms at all when you have a tool like Weka around. If I were you, I would run through a list of supervised learning algorithms (I don't completely understand whey people are suggesting unsupervised learning first when this is so clearly a classification problem) using 10-fold (or k-fold) cross validation, which is the default in Weka if I remember, and see what results you get! I would try:
-Neural Nets
-SVMs
-Decision Trees (this one worked really well for me when I was doing a similar problem)
-Boosting with Decision trees/stumps
-Anything else!
Weka makes things so easy and you really can get some useful information. I just took a machine learning class and I did exactly what you're trying to do with the algorithms above, so I know where you're at. For me the boosting with decision stumps worked amazingly well. (BTW, boosting is actually a meta-algorithm and can be applied to most supervised learning algs to usually enhance their results.)
A nice thing aobut using Decision Trees (if you use the ID3 or similar variety) is that it chooses the attributes to split on in order of how well they differientiate the data - in other words, which attributes determine the classification the quickest basically. So you can check out the tree after running the algorithm and see what attribute of a comic book most strongly determines the price - it should be the root of the tree.
Edit: I think Yuval is right, I wasn't paying attention to the problem of discretizing your price value for the classification. However, I don't know if regression is available in Weka, and you can still pretty easily apply classification techniques to this problem. You need to make classes of price values, as in, a number of ranges of prices for the comics, so that you can have a discrete number (like 1 through 10) that represents the price of the comic. Then you can easily run classification it.