What machine learning techniques can be used to make a model if some attributes change over time? For example predicting prices of a hotel depends on the number of tourists in the city which is time dependent i.e. it changes from time to time.
Also, if we have a good trained model on some static data, then what are the ways to update the model if some data is changed except retraining the model on complete data again?
Regarding the first question, I would just add a feature indicating time. For instance, hotel X will appear in few data records, each one differs in the value of it's "Month" feature (the data-point of August might have an higher price from the one of December). This way the model will take into consideration the time of the year.
Regarding the second question, unless you're using reinforcement learning / online learning, which is used to train models from an oncoming sequences of samples, I don't see a way to change the data without having the train to model again.
Related
The question: Is it normal / usual / professional to use the past of the labels as features?
I could not find anything reliable on this, although it is a basic question.
Edited: Please mind, this is not a time-series question, I have deleted the time-series tag now and I changed the question. This question is about features that change regularly over time, yes! But we do not create a time-series from this, as there are many other features as well which are not like the label and are also important features in the model. Now please think of using past labels as normal features without a time-series approach.
I try to predict a certain month of data that is available monthly, thus a time-series, but I am not using it as a time-series, it is just monthly avaiable data of various different features.
It is a classification model, and now I want to predict a label column of a selected month of that time-series. The previous months before the selected label month are now the point of the question.
I do not want to just drop the past months of the label just because they are "almost" a label (or in other words: they were just the label columns of the preceding models in time). I know the past of the label, why not considering it as features as well?
My predictions are of course much better when adding the past labels of the time-series of labels to the features. This is logical as the labels usually do not change so much from one month to the other and thus can be predicted very well if you have fed the data with the past of the label. It would be strange not to use such "past labels" as features, as any simple time-series regression would then be better than the ml model.
Example: Let's say I predict the IQ test result of a person, and I use her past IQ test results as features in addition to other normal "non-label" features like age, education aso. I use the first 11 months of "past labels" of a year as features in addition to my normal "non-label" features. I predict the label of the 12th month.
Predicting the label of the 12th month works much better if you add the past of the labels to the features - obviously. This is because the historical labels, if there are any, are of course better indicators of the final outcome than normal columns like age and education.
Possibly related p.s.:
p.s.1: In auto-regressive models, the past of the dependent variable can well be used as independent variable, see: https://de.wikipedia.org/wiki/Regressionsanalyse
p.s.2: In ML you can perhaps just try any features and take what gives you the best results, a bit like >Good question, try them [feature selection methods] all and see what works best< in https://machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/ >If the features are relevant to the outcome, the model will figure out how to use them. Or most models will.< The same is said in Does the feature selection matter for learning algorithm with regularization?
p.s.3: Also probably relevant is the problem of multicollinearity: https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/ though multicollinearity is said to be no issue for the prediction: >Multicollinearity affects the coefficients and p-values, but it does not influence the predictions, precision of the predictions, and the goodness-of-fit statistics. If your primary goal is to make predictions, and you don’t need to understand the role of each independent variable, you don’t need to reduce severe multicollinearity.
It is perfectly possible and also good practice to include past label columns as features, though it depends on your question: do you want to explain the label only with other features (on purpose), or do you want to consider other and your past label columns to get the next label predicted, as a sort of adding a time-series character to the model without using a time-series?
The sequence in time is not even important, as long as all of such monthly columns are shifted in time consistently by the same time when going over to the predicting set. The model does not care if it is just January and February of the same column type, for the model, every feature is isolated.
Example: You can perfectly run a random forest model on various features, including their past label columns that repeat the same column type again and again, only representing different months. Any month's column can be dealt with as an independent new feature in the ml model, the only importance is to shift all of those monthly columns by the exactly same period to reach a consistent predicting set. In other words, obviously you should avoid replacing January with March column when you go from a training set January-June to a predicting set February-July, instead you must replace January with February of course.
Update 202301: model name is "walk-forward"
This model setup is called "walk-forward", see Why isn’t out-of-time validation more ubiquitous? --> option 3 almost at the bottom of the page.
I got this from a comment at Splitting Time Series Data into Train/Test/Validation Sets.
In the following, it shows only training and testing set. It writes "validation set", but it is known that this gets mixed up all over the place, see What is the Difference Between Test and Validation Datasets?, and it must be meant as the testing set in the default understanding of it.
Thus, with the right wording, it is:
This should be the best model for labels that become features in time.
validation set in a "walk-forward" model?
As you can see in the model, no validation set is needed since the test data must be biased "forward" in time, that is the whole idea of predicting the "step forward in time", and any validation set would have to be in that same biased artificial future - which is already the past at the time of training, but the model does not know this.
The validation happens by default, without a needed dataset split, during the walk-forward, when the model learns again and again to predict the future and the output metrics can be put against each other. As the model is to predict the time-biased future, there is no need to prove that or how the artificial future is biased and sort of "overtrained by time". It is the aim of the model to have the validation in the artificial future and predict the real future as a last step only.
But then, why not still having a validation set on top of this, at least if it is just a small k-fold validation? It could play a role if the testing set has a few strong changes that happen in small time windows but which are still important to be predicted, or at least hinted at, but should also not be overtrained within each training step. The validation set would hit some of these time windows and might show whether the model can handle them well enough. Any other method than k-fold would shrink the power of the model too much. The more you take away from the testing set during training, the less it can predict the future.
Wrap up:
Try it out, and in doubt, leave the validation aside and judge upon the model by checking its metrics over time, during the "walk-forward". This model is not like the others.
Thus, in the end, you can, but you do not have to, split a k-fold validation from the testing set. That would look like:
After predicting a lot of known futures, the very last step in time is then the prediction of the unknown future.
This also answers Does the training+testing set have to be different from the predicting set (so that you need to apply a time-shift to ALL columns)?.
Say I have a data set of students with features such as income level, gender, parents' education levels, school, etc. And the target variable is say, passing or failing a national exam. We can train a machine learning model to predict, given these values whether a student is likely to pass or fail (say in sklearn, using predict_prob we can say the probability of passing)
Now say I have a different set of information which has nothing to do with the previous data set, which includes the schools and percentage of students from that particular school who has passed that national exam last year and years before. say, schoolA: 10%, schoolB: 15%, etc.
How can I use this additional knowledge to improve my model. For sure this data is valuable. (Students from certain schools have a higher chance of passing the exam due to their educational facilities, qualified staff, etc.).
Do i some how add this information as a new feature to the data set? If so what is the recommend way. Or do I use this information after the model prediction and somehow combine these to get a final probability ? Obviously an average or a weighted average doesn't work due to the second data set having probabilities in the range below 20% which then drags the total probability very low. How do data scientist usually incorporate this kind of prior knowledge? Thank you
You can try different ways to add this data and see if your model will be able to learn on this set. More likely you'll see right away, that this additional data will just confuse the model. Mostly because you're already providing more precise data on each student of the school and the model has more freedom to use this information.
But artificial neural network training is all about continuous trials and errors, so you definitely should try to train it with all possible data you can imagine to see if it'll be able to get a descent error in the end.
Use the average pass percentage of the students' school as a new feature of each student is worth to try.
This might sound like an elementary question but I am having a major confusion regarding Training Set and Test.
When we use Supervised learning techniques such as Classification to predict something a common practice is to split the dataset into two parts training and test set. The training set will have a predictor variable, we train the model on the dataset and "predict" things.
Let's take an example. We are going to predict loan defaulters in a bank and we have the German credit data set where we are predicting defaulters and non- defaulters but there is already a definition column which says whether a customer is a defaulter or Non-defaulter.
I understand the logic of prediction on UNSEEN data, like the Titanic survival data but what is the point of prediction where a class is already mentioned, such as German credit lending data.
As you said, the idea is to come up a model that you can predict UNSEEN data. The test data is only used to measure the performance of your model created through training data. You want to make sure the model you comes up does not "overfit" your training data. That's why the testing data is important. Eventually, you will use the model to predict whether a new loaner is going to default or not, thus making a business decision whether to approve the loan application.
The reason why they include the defaulted values is so that you can verify that the model is working as expected and predicting the correct results. Without which there is no way for anyone to be confident that their model is working as expected.
The ultimate purpose of training a model is to apply it to what you call UNSEEN data.
Even in your German credit lending example, at the end of the day you will have a trained model that you could use to predict if new - unseen - credit applications will default or not. And you should be able to use it in the future for any new credit application, as long as you are able to represent the new credit data in the same format you used to train your model.
On the other hand, the test set is just a formalism used to estimate how good the model is. You cannot know for sure how accurate your model it is going to be with future credit applications, but what you can do is to save a small part of your training data, and use it only to check the model's performance after it has been built. That's what you would call the test set (or more precisely, a validation set).
Im new to &investigating Machine Learning. I have a use case & data but I am unsure of a few things, mainly how my model will run, and what model to start with. Details of the use case and questions are below. Any advice is appreciated.
My Main question is:
When basing a result on scores that are accumulated over time, is it possible to design a model to run on a continuous basis so it gives a best guess at all times, be it run on day one or 3 months into the semester?
What model should I start with? I was thinking a classifier, but ranking might be interesting also.
Use Case Details
Apprentices take a semesterized course, 4 semesters long, each 6 months in duration. Over the course of a semester, apprentices perform various operations and processes & are scored on how well they do. After each semester, the apprentices either have sufficient score to move on to semester 2, or they fail.
We are investigating building a model that will help identify apprentices who are in danger of failing, with enough time for them to receive help.
Each procedure is assigned a complexity code of simple, intermediate or advanced, and are weighted by complexity.
Regarding Features, we have the following: -
Initial interview scores
Entry Exam Scores
Total number of simple procedures each apprentice performed
Total number of intermediate procedures each apprentice performed
Total number of advanced procedures each apprentice performed
Average score for each complexity level
Demograph information (nationality, age, gender)
I am unsure of is how the model will work and when we will run it. i.e. - if we run it on day one of the semester, I assume everyone will fail as everyone has procedure scores of 0
Current plan is to run the model 2-3 months into each semester, so there is enough score data & also enough time to help any apprentices who are in danger of failing.
This definitely looks like a classification model problem:
y = f(x[0],x[1], ..., x[N-1])
where y (boolean output) = {pass, fail} and x[i] are different features.
There is a plethora of ML classification models like Naive Bayes, Neural Networks, Decision Trees, etc. which can be used depending upon the type of the data. In case you are looking for an answer which suggests a particular ML model, then I would need more data for the same. However, in general, this flow-chart can be helpful in selection of the same. You can also read about Model Selection from Andrew-Ng's CS229's 5th lecture.
Now coming back to the basic methodology, some of these features like initial interview scores, entry exam scores, etc. you already know in advance. Whereas, some of them like performance in procedures are known over the semester.
So, there is no harm in saying that the model will always predict better towards the end of each semester.
However, I can make a few suggestions to make it even better:
Instead of taking the initial procedure-scores as 0, take them as a mean/median of the past performances in other procedures by the subject-apprentice.
You can even build a sub-model to analyze the relation between procedure-scores and interview-scores as they are not completely independent. (I will explain this sentence in the later part of the answer)
However, if the semester is very first semester of the subject-apprentice, then you won't have such data already present for that apprentice. In that case, you might need to consider the average performances of other apprentices with similar profiles as the subject-apprentice. If the data-set is not very large, K Nearest Neighbors approach can be quite useful here. However, for large data-sets, KNN suffers from the curse of dimensionality.
Also, plot a graph between y and different variables x[i], so as to see the independent variation of y with respect to each variable.
Most probably (although it's just a hypotheses), y will depend more the initial variables in comparison the variables achieved later. The reason being that the later variables are not completely independent of the former variables.
My point is, if a model can be created to predict the output of a semester, then, a similar model can be created to predict just the output of the 1st procedure-test.
In the end, as the model might be heavily based on demographic factors and other things, it might not be a very successful model. For the same reason, we cannot accurately predict election results, soccer match results, etc. As they are heavily dependent upon real-time dynamic data.
For dynamic predictions based on different procedure performances, Time Series Analysis can be a bit helpful. But in any case, the final result will heavily dependent on the apprentice's continuity in motivation and performance which will become more clear towards the end of each semester.
I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document (with publication date) whether or not it belongs to T, where the publication date might be in the past or in the future. Assume that I have decomposed each training document's text into a set of features (e.g., TF-IDF of words or n-grams) suitable for analysis by an appropriate binary classification algorithm provided by a library like Weka (for instance, multinomial naive Bayes, random forests, or SVM). The concept to be learned exhibits multiple seasonality; i.e., the prior probability that an arbitrary document published on a given date belongs to T depends heavily on when the date falls in a 4-year cycle (due to elections), where it falls in an annual cycle (due to holidays), and on the day of the week.
My research indicates that classification algorithms generally assume (as part of their statistical models) that training data is randomly sampled from the same pool of data that the model will ultimately be applied to. When the distribution of classes in the training data differs substantially from the known distribution in the wild, this leads to the so-called "class imbalance" problem. There are ways of compensating for this, including over-sampling underrepresented classes, under-sampling overrepresented classes, and using cost-sensitive classification. This allows a model creator to implicitly specify the prior probability that a new document will be positively classified, but importantly (and unfortunately for my purposes), this prior probability is assumed to be equal for all new documents.
I require more flexibility in my model. Because of the concept's seasonality, when classifying a new document, the model must explicitly take the publication date into account when determining the prior probability that the document belongs to T, and when the model calculates the posterior probability of belonging to T in light of the document's features, this prior probability should be properly accounted for. I am looking for a classifier implementation that either (1) bakes sophisticated regression of prior probabilities based on dates into the classifier, or (2) can be extended with a user-specified regression function that takes a date as input and gives the prior probability as output.
I am most familiar with the Weka library, but am open to using other tools if they are appropriate to the job. What is the most straightforward way of accomplishing this task?
Edit (in response to Doxav's point #2):
My concern is that date-based attributes should not be used for learning rules about when the topic applies, rather, they should be used only for determining the prior probability of whether the topic applies. Here's a concrete example: suppose that the topic T is "Christmas". A story published in July is indeed much less likely to be about Christmas than a story published in December. But what makes a story about Christmas is the textual content of the story, not when it was published. The relationship between publication date and "being about Christmas" is mere correlation, and therefore only useful for calculating the prior probability of an arbitrary story on an arbitrary date being about Christmas. By comparison, the relationship between TF-IDF (for some term in the story text) and "being about Christmas" is inherent and causative, and therefore worthy of incorporation into our model of what it means for a story to be about Christmas.
It seems like it can be simplified into typical ML problems: text classification + imbalanced data + seasonality identification + architecture + typical batch/offline vs stream/online learning :
Text classification: https://www.youtube.com/watch?v=IY29uC4uem8 is a good tutorial on text classification with Weka and covers imbalance data issue.
Seasonality identification: the goal is to enable the model to learn rules/inference on some different time attributes, so we should ease its job by extracting best known useful attributes. It means extracting typical date cycles (ie. week day, day of month, month, year...) and, if possible, also merge it with other more specific cycles or events (ie. elections, holidays, any custom cycle or frequent event). If you expect the model to learn on time series/sequences, you should create some lag data (attributes who happened before or statistics on recent time interval). It can be good to remove the date itself or any data which would make biase the model construction.
I don't know if you plan to deliver this as a service, but this can be of good inspiration: http://fr.slideshare.net/TraianRebedea/autonomous-news-clustering-and-classification-for-an-intelligent-web-portal .
Typical batch/offline vs Stream / online learning: Apparently you already know Weka which focuses on batch/offline learning. I don't know the size of your data and if you plan to continuously process new data and rebuild models, then you could consider moving to stream processing and online learning. Therefore, you could move to MOA which is very close to Weka but dedicated to stream classification, or use new streaming features of the latest version of Weka (steam processing and new online learners).
UPDATE 1 ; I read your comment and I see different solutions:
answer #2 is still one possible solution for your need even if it is not optimal. Getting an attribute indicating it's Christmas period will set an higher probability to tag it as a Christmas topic, same for the TF-IDF of the "word" Chritmas, BUT only both attributes together will set the max classification prob very highly to be Christmas.
you can use an attribute providing a seasonal weight for each word: TF-IDF with time weight, or use current Google Trends data for each word.
if you want a state of the art adaptive prior upon context you could look into hierarchical Bayesian models and smoothing from NLP solutions. It won't be Weka then and not as fast to test.