I currently have a basket of 200 investment portfolios which are partitioned into 3 styles: Long, Intermediate, and Short. Each portfolio has 10 interesting features X which define its style. I want to create a system whereby I can identify portfolios that are out of style and that that need some work to get them back into style.
My thought is to use a probabilistic framework to solve this problem. This will reduce my 10 interesting features down to a single number which will determine whether or not the portfolio needs attention. To this end I'm currently creating a binary classifier on each style using a neural network with a sigmoid activation function to estimate probabilities. If the probability that a portfolio belongs to a certain style is less than 75%, I will transact to bring the portfolio back into style.
Is this the best framework for an anomaly detection system given what I am trying to accomplish? A multinomial classifier could be another option, but it is quite similar the simple binary classifier I described above.
Best
For anyone curious, I ended up using a one-sided support vector machine. This I decided was best as I had very few negative examples to work with.
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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 want to build an RL agent which can justify if a handwritten word is written by the legitimate user or not. The plan is as follow:
Let's say I have written any word 10 times and extracted some geometrical properties for all of them to use as features. Then I have trained an RL agent to learn to take the decision on the basis of the differences between geometrical properties of new and the old 10 handwritten texts. Reward is assigned for correct identification and nothing or negative for incorrect one.
Am I going in the right direction or I am missing anything which is vital? Is it possible to train the agent with only 10 samples? Actally as a new student of RL, I am confused about use case of RL; if it is best fit for game solving and robotic problems or it is also suitable for predicting on the basis of training.
Reinforcement learning would be used over time. If you were following the stroke of the pen, over time, to find out which way it was going that would be more reinforcement learning's wheelhouse. The time dimension (or over a series of states) is why it's used in games like Starcraft II.
You are talking about taking a picture of the text that was written and eventually classifying it into a boolean (Good or Not). You are looking for more Convolutional neural networks to solve your problem (those types of algos are good for pictures).
Eventually you won't be able to tell. There are techniques with GAN's (Generative Adversarial Networks) that can train with your discriminator and finally figure out the pattern it's looking for and fool it. But this sounds good as a homework problem.
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.
I'm playing around with writing a web crawler that scans for a specific set of keywords and then assigns a global score to each domain it encounters based on a cumulative score I assigned to each keyword (programming=1, clojure=2, javascript=-1, etc...).
I have set up my keyword scoring on a sliding scale of -10 to 10 and I have based my initial values on my own assumptions about what is and is not relevant.
I feel that my scoring model may be flawed, and I would prefer to feed a list of domains that match the criteria I'm trying to capture into an analysis tool and optimize my keyword weights based on some kind of statistical analysis.
What would be an appropriate analysis technique to generate an optimal scoring model for a list of "known good domains"? Is this problem suited for bayesian learning, monte carlo simulation, or some other technique?
So, given a training set of relevant and irrelevant domains, you'd like to build a model which classifies new domains to one of these categories. I assume the features you will be using are the terms appearing in the domains, i.e. this is can be framed as a document classification problem.
Generally, you are correct in assuming that letting statistical-based machine learning algorithms to do the "scoring" for you works better than assigning manual scores to keywords.
A simple way to approach the problem would be to using Bayesian learning, and specifically, Naive Bayes might be a good fit.
After generating a dataset from the domains you've manually tagged (e.g. collecting several pages from each domain and treating each as a document), you can experiment various algorithms using one of the machine learning frameworks, e.g. WEKA.
A primer on how to handle and load text documents to WEKA can be found here. After the data is loaded, you can use the framework to experiment with various classification algorithms, e.g. Naive Bayes, SVM, etc. Once you've found the method best fitting your needs, you can export the resulting model and use it via WEKA's Java API.
I am implementing K nearest neighbour algorithm for a very sparse data. I want to calculate the distance between a test instance and each sample in the training set, but I am confused.
Because most of the features in training samples don't exist in test instance or vice versa (missing features).
How can I compute the distance in this situation?
To make sure I'm understanding the problem correctly: each sample forms a very sparsely filled vector. The missing data is different between samples, so it's hard to use any Euclidean or other distance metric to gauge similarity of samples.
If that is the scenario, I have seen this problem show up before in machine learning - in the Netflix prize contest, but not specifically applied to KNN. The scenario there was quite similar: each user profile had ratings for some movies, but almost no user had seen all 17,000 movies. The average user profile was quite sparse.
Different folks had different ways of solving the problem, but the way I remember was that they plugged in dummy values for the missing values, usually the mean of the particular value across all samples with data. Then they used Euclidean distance, etc. as normal. You can probably still find discussions surrounding this missing value problem on that forums. This was a particularly common problem for those trying to implement singular value decomposition, which became quite popular and so was discussed quite a bit if I remember right.
You may wish to start here:
http://www.netflixprize.com//community/viewtopic.php?id=1283
You're going to have to dig for a bit. Simon Funk had a little different approach to this, but it was more specific to SVDs. You can find it here: http://www.netflixprize.com//community/viewtopic.php?id=1283
He calls them blank spaces if you want to skip to the relevant sections.
Good luck!
If you work in very high dimension space. It is better to do space reduction using SVD, LDA, pLSV or similar on all available data and then train algorithm on trained data transformed that way. Some of those algorithms are scalable therefor you can find implementation in Mahout project. Especially I prefer using more general features then such transformations, because it is easier debug and feature selection. For such purpose combine some features, use stemmers, think more general.