I serve as internal auditor in few clients ,one of my client has thousands of employees in different location, most of them in the head office, the client looks for corporate control for the salary monitoring
is it make sense to use the regression method in order to find outliers,
potential parameter can be -years of experience, gender, level/rank etc
I planned to go over all the monthly payroll and look for significant outliers ,because of the differences between the global location, it might be a good idea to focus only in the head office
the idea is to train the model for previous months average and test it for the current month
what do you think is too much effort or theoretical ? or can have a good chance to bring value ?
thank you
This answers your question regarding the regression method to use. It makes sense to only use data from the head office, as adding data from different geographies will require you to add more data around general demographics, which you can avoid for a proof of concept.
Coming to the problem itself, you'll need to provide a better explanation of how you're defining outliers. Are you looking for mistakes in payroll? Or are you looking for people who make significantly more/less than their peers? You'll only be able to decide on a modelling framework once you get clarity the basic definitions.
Also, you might want to consider statistical significance tests like Grubbs test (more information on tests here) first, before moving to machine learning approaches. They're easier to set up and explain to non-practitioners.
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I used machine learning to train depression related sentences. And it was LinearSVC that performed best. In addition to LinearSVC, I experimented with MultinomialNB and LogisticRegression, and I chose the model with the highest accuracy among the three. By the way, what I want to do is to be able to think in advance which model will fit, like ml_map provided by Scikit-learn. Where can I get this information? I searched a few papers, but couldn't find anything that contained more detailed information other than that SVM was suitable for text classification. How do I study to get prior knowledge like this ml_map?
How do I study to get prior knowledge like this ml_map?
Try to work with different example datasets on different data types by using different algorithms. There are hundreds to be explored. Once you get the good grasp of how they work, it will become more clear. And do not forget to try googling something like advantages of algorithm X, it helps a lot.
And here are my thoughts, I think I used to ask such questions before and I hope it can help if you are struggling: The more you work on different Machine Learning models for a specific problem, you will soon realize that data and feature engineering play the more important parts than the algorithms themselves. The road map provided by scikit-learn gives you a good view of what group of algorithms to use to deal with certain types of data and that is a good start. The boundaries between them, however, are rather subtle. In other words, one problem can be solved by different approaches depending on how you organize and engineer your data.
To sum it up, in order to achieve a good out-of-sample (i.e., good generalization) performance while solving a problem, it is mandatory to look at the training/testing process with different setting combinations and be mindful with your data (for example, answer this question: does it cover most samples in terms of distribution in the wild or just a portion of it?)
I would like to know which model should I choose to forecast monthly sales. should I go for regression approaches or time-series methods for small 1.5-year data?
One of the first steps I would make is to clearly determine how many features you have.
In case of Univariate forecasting (observations in time of a single variable), you would most likely resort to even statistical approaches, such as ARIMA/SARIMA(I assume the concept of seasonality is known; if not, please read on properties of time series here : https://www.dummies.com/programming/big-data/data-science/key-properties-of-a-time-series-in-data-analysis/.
If you have multiple features(observations in time of multiple variables), you could first try with a VAR(vector autoregression).
Try these models at first, and only then proceed to more complicated ones such as LSTM/CNNs
Supporting #Nicolae Petridean's affirmation, the principle of Occam's Razor should always be applied: start with simple models and only after having tried several simpler ones should you progress to deep learning techniques.
Also, bear in mind that in the case of the latter, you will need much more data as compared to simpler statistical/mathematical models or even classical machine learning ones.
Depending on the data that you have either one or the other might work. Or other techniques. Try 2 simple models using each of the 2 techniques, and validate them against a common validation dataset. This way you will have your answer. Nobody can answer to your question unless has quite some good insights into the data that you have for training. Out of my belly I would probably start with a regression but in the end I assume you will end up using something else. It is always a good option to start with simple models first to better understand the problem and then progressively fine tune or do other tricks and more complicated models, depending on what the models you already have learn or not.
Have a look at this Kaggle competition : https://www.kaggle.com/c/competitive-data-science-predict-future-sales
Check several notebooks from there and maybe you will understand more on what works or does not work in this kind of prediction.
Link to notebooks : https://www.kaggle.com/c/competitive-data-science-predict-future-sales/notebooks
[I'm approaching this as an outsider to machine learning. It just seems like a classification problem which I should be able to solve with fairly good accuracy with Machine Larning.]
Training Dataset:
I have millions of URLs, each tagged with a particular category. There are limited number of categories (50-100).
Now given a fresh URL, I want to categorize it into one of those categories. The category can be determined from the URL using conventional methods, but would require a huge unmanageable mess of pattern matching.
So I want to build a box where INPUT is URL, OUTPUT is Category. How do I build this box driven by ML?
As much as I would love to understand the basic fundamentals of how this would work out mathematically, right now much much more focussed on getting it done, so a conceptual understanding of the systems and processes involved is what I'm looking to get. I suppose machine learning is at a point where you can approach reasonably straight forward problems in that manner.
If you feel I'm wrong and I need to understand the foundations deeply in order to get value out of ML, do let me know.
I'm building this inside an AWS ecosystem so I'm open to using Amazon ML if it makes things quicker and simpler.
I suppose machine learning is at a point where you can approach reasonably straight forward problems in that manner.
It is not. Building an effective ML solution requires both an understanding of problem scope/constraints (in your case, new categories over time? Runtime requirements? Execution frequency? Latency requirements? Cost of errors? and more!). These constraints will then impact what types of feature engineering / processing you may look at, and what types of models you will look at. Your particular problem may also have issues with non I.I.D. data, which is an assumption of most ML methods. This would impact how you evaluate the accuracy of your model.
If you want to learn enough ML to do this problem, you might want to start looking at work done in Malicious URL classification. An example of which can be found here. While you could "hack" your way to something without learning more about ML, I would not personally trust any solution built in that manner.
If you feel I'm wrong and I need to understand the foundations deeply in order to get value out of ML, do let me know.
Okay, I'll bite.
There are really two schools of thought currently related to prediction: "machine learners" versus statisticians. The former group focuses almost entirely on practical and applied prediction, using techniques like k-fold cross-validation, bagging, etc., while the latter group is focused more on statistical theory and research methods. You seem to fall into the machine-learning camp, which is fine, but then you say this:
As much as I would love to understand the basic fundamentals of how this would work out mathematically, right now much much more focussed on getting it done, so a conceptual understanding of the systems and processes involved is what I'm looking to get.
While a "conceptual understanding of the systems and processes involved" is a prerequisite for doing advanced analytics, it isn't sufficient if you're the one conducting the analysis (it would be sufficient for a manager, who's not as close to the modeling).
With just a general idea of what's going on, say, in a logistic regression model, you would likely throw all statistical assumptions (which are important) to the wind. Do you know whether certain features or groups shouldn't be included because there aren't enough observations in that group for the test statistic to be valid? What can happen to your predictions and hypotheses when you have high variance-inflation factors?
These are important considerations when doing statistics, and oftentimes people see how easy it is to do from sklearn.svm import SVC or somthing like that and run wild. That's how you get caught with your pants around your ankles.
How do I build this box driven by ML?
You don't seem to have even a rudimentary understanding of how to approach machine/statistical learning problems. I would highly recommend that you take an "Introduction to Statistical Learning"- or "Intro to Regression Modeling"-type course in order to think about how you translate the URLs you have into meaningful features that have significant power predicting URL class. Think about how you can decompose a URL into individual pieces that might give some information as to which class a certain URL pertains. If you're classifying espn.com domains by sport, it'd be pretty important to parse nba out of http://www.espn.com/nba/team/roster/_/name/cle, don't you think?
Good luck with your project.
Edit:
To nudge you along, though: every ML problem boils down to some function mapping input to output. Your outputs are URL classes. Your inputs are URLs. However, machines only understand numbers, right? URLs aren't numbers (AFAIK). So you'll need to find a way to translate information contained in the URLs to what we call "features" or "variables." One place to start, there, would be one-hot encoding different parts of each URL. Think of why I mentioned the ESPN example above, and why I extracted info like nba from the URL. I did that because, if I'm trying to predict to which sport a given URL pertains, nba is a dead giveaway (i.e. it would very likely be highly predictive of sport).
I'm looking for some advice in the problem of classifying users into various groups based on there answers to a sign up process.
The idea is that these classifications will group people with similar travel habits, i.e. adventurous, relaxing, foodie etc. This shouldn't be a classification known to the user, so isn't as simple as just asking what sort of holidays they like ( The point is to remove user bias/not really knowing where to place yourself).
The way I see it working is asking questions such as apps they use, accounts they interact with on social media (gopro, restaurants etc) , giving some scenarios and asking which sounds best, these would be chosen from a set provided to them, hence we have control over the variables. The main problem I have is how to get numerical values associated to each of these.
I've looked into various Machine learning algorithms and have realised this is most likely a clustering problem but I cant seem to figure out how to use this style of question to assign a value to each dimension that will actually give a useful categorisation.
Another question I have is whether there is some resources where I could find information on the sort of questions to ask users to gain information that'd allow classification like this.
The sort of process I envision is one similar to https://www.thread.com/signup/introduction if anyone is familiar with it.
Any advice welcomed.
The problem you have at hand is that you want to calculate a similarity measure based on categorical variables, which is the choice of their apps, accounts etc. Unless you measure the similarity of these apps with respect to an attribute such as how foodie is the app, it would be a hard problem to specify. Also, you would need to know all the possible states a categorical variable can assume to create a similarity measure like this.
If the final objective is to recommend something that similar people (based on app selection or social media account selection) have liked or enjoyed, you should look into collaborative filtering.
If your feature space is well defined and static (known apps, known accounts, limited set with few missing values) then look into content based recommendation systems, something as simple as Market Basket Analysis can give you a reasonable working model.
Else if you really want to model the system with a bunch of features that can assume random states, this could be done with multivariate probabilistic models, if the structure (relationships and influences between features) is well defined, you could benefit from Probabilistic Graphical Models, such as Bayesian Networks.
You really do need to define your problem better before you start solving it though.
You can use prime numbers. If each choice on the list of all possible choices is assigned a different prime, and the user's selection is saved as a product, then you will always know if the user has made a particular choice if the modulo of selection/choice is 0. Beauty of prime numbers, voila!
My question is related to the project I've just started working on, and it's a ChatBot.
The bot I want to build has a pretty simple task. It has to automatize the process of purchasing movie tickets. This is pretty close domain and the bot has all the required access to the cinema database. Of course it is okay for the bot to answer like “I don’t know” if user message is not related to the process of ordering movie tickets.
I already created a simple demo just to show it to a few people and see if they are interested in such a product. The demo uses simple DFA approach and some easy text matching with stemming. I hacked it in a day and it turned out that users were impressed that they are able to successfully order tickets they want. (The demo uses a connection to the cinema database to provide users all needed information to order tickets they desire).
My current goal is to create the next version, a more advanced one, especially in terms of Natural Language Understanding. For example, the demo version asks users to provide only one information in a single message, and doesn’t recognize if they provided more relevant information (movie title and time for example). I read that an useful technique here is called "Frame and slot semantics", and it seems to be promising, but I haven’t found any details about how to use this approach.
Moreover, I don’t know which approach is the best for improving Natural Language Understanding. For the most part, I consider:
Using “standard” NLP techniques in order to understand user messages better. For example, synonym databases, spelling correction, part of speech tags, train some statistical based classifiers to capture similarities and other relations between words (or between the whole sentences if it’s possible?) etc.
Use AIML to model the conversation flow. I’m not sure if it’s a good idea to use AIML in such a closed domain. I’ve never used it, so that’s the reason I’m asking.
Use a more “modern” approach and use neural networks to train a classifier for user messages classification. It might, however, require a lot of labeled data
Any other method I don’t know about?
Which approach is the most suitable for my goal?
Do you know where I can find more resources about how does “Frame and slot semantics” work in details? I'm referring to this PDF from Stanford when talking about frame and slot approach.
The question is pretty broad, but here are some thoughts and practical advice, based on experience with NLP and text-based machine learning in similar problem domains.
I'm assuming that although this is a "more advanced" version of your chatbot, the scope of work which can feasibly go into it is quite limited. In my opinion this is a very important factor as different methods widely differ in the amount and type of manual effort needed to make them work, and state-of-the-art techniques might be largely out of reach here.
Generally the two main approaches to consider would be rule-based and statistical. The first is traditionally more focused around pattern matching, and in the setting you describe (limited effort can be invested), would involve manually dealing with rules and/or patterns. An example for this approach would be using a closed- (but large) set of templates to match against user input (e.g. using regular expressions). This approach often has a "glass ceiling" in terms of performance, but can lead to pretty good results relatively quickly.
The statistical approach is more about giving some ML algorithm a bunch of data and letting it extract regularities from it, focusing the manual effort in collecting and labeling a good training set. In my opinion, in order to get "good enough" results the amount of data you'll need might be prohibitively large, unless you can come up with a way to easily collect large amounts of at least partially labeled data.
Practically I would suggest considering a hybrid approach here. Use some ML-based statistical general tools to extract information from user input, then apply manually built rules/ templates. For instance, you could use Google's Parsey McParseface to do syntactic parsing, then apply some rule engine on the results, e.g. match the verb against a list of possible actions like "buy", use the extracted grammatical relationships to find candidates for movie names, etc. This should get you to pretty good results quickly, as the strength of the syntactic parser would allow "understanding" even elaborate and potentially confusing sentences.
I would also suggest postponing some of the elements you think about doing, like spell-correction, and even stemming and synonyms DB - since the problem is relatively closed, you'll probably have better ROI from investing in a rule/template-framework and manual rule creation. This advice also applies to explicit modeling of conversation flow.