I have a data set that has weighted scores based on gender and age profiles. I also have region data broken up into 7 states. I basically want to exclude one of those states and apply additional weights based on state to come up with a new "overall" score.
Manual excel calculations is only way I can think of doing this.
I need to take scores that already have a variable weight applied and add an additional weight dependent on region.
SPSS Statistics only allows a single weight variable to be applied at any one time, so Kevin Troy's comments are correct: you'll have to combine things into a single weight. If the data are properly combined into a single file you may find the Rake Weights extension that's installed with the Python Essentials useful, as you can specify multiple variables as inputs to the overall weighting scheme and have the weights calculated for you. If you're not familiar with the theory behind this, look up raking or rim weighting.
Related
I am having six feature columns and one target column, which is imbalanced.
Can I make oversampling method like ADASYN or SMOTE by creating synthetic records only for the four columns X1,X2,X3,X4 by copying exactly the same as constant (Month, year column)
Current one:
Expected one: It can create synthetic records by up-sampling target class '1' but the number of records can increase but the added records should have month and years (unchanged as shown below )
From a programming perspective, an identical question asked in the relevant Github repo back in 2017 was answered negatively:
[Question]
I have a data frame that I want to apply smote to but I wish to only use a subset of the columns. The other columns contain additional data for each sample and I want each new sample to contain the original info as well
[Answer]
There is no way to do that apart of extracting the column in a new matrix and process it with SMOTE.
Even if you generate a new samples you have to decide what to put as values there so I don't see how such feature can be added
Answering from a modelling perspective, this is not a good idea and, even if you could find a programming workaround, you should not attempt it - and arguably, this is the reason why the developer of imbalanced-learn above was dismissive even in the thought of adding such a feature in the SMOTE implementation.
Why is that? Well, synthetic oversampling algorithms, like SMOTE, essentially use some variant of a k-nn approach in order to create artificial samples "between" the existing ones. Given this approach, it goes without saying that, in order for these artificial samples to be indeed "between" the real ones (in a k-nn sense), all the existing (numerical) features must be taken into account.
If, by employing some programming alchemy, you manage at the end to produce new SMOTE samples based only on a subset of your features, putting the unused features back in will destroy any notion of proximity and "betweenness" of these artificial samples to the real ones, thus compromising the whole enterprise by inserting a huge bias in your training set.
In short:
If you think your Month and year are indeed useful features, just include them in SMOTE; you may get some nonsensical artificial samples, but this should not be considered a (big) problem for the purpose here.
If not, then maybe you should consider removing them altogether from your training.
I have implemented a recommender system based upon matrix factorization techniques. I want to evaluate it.
I want to use 10-fold-cross validation with All-but-one protocol (https://ai2-s2-pdfs.s3.amazonaws.com/0fcc/45600283abca12ea2f422e3fb2575f4c7fc0.pdf).
My data set has the following structure:
user_id,item_id,rating
1,1,2
1,2,5
1,3,0
2,1,5
...
It's confusing for me to think how the data is going to be splitted, because I can't put some triples (user,item,rating) in the testing set. For example, if I select the triple (2,1,5) to the testing set and this is the only rating user 2 has made, there won't be any other information about this user and the trained model won't predict any values for him.
Considering this scenario, how should I do the splitting?
You didn't specify a language or toolset so I cannot give you a concise answer that is 100% applicable to you, but here's the approach I took to solve this same exact problem.
I'm working on a recommender system using Treasure Data (i.e. Presto) and implicit observations, and ran into a problem with my matrix where some users and items were not present. I had to re-write the algorithm to split the observations into train and test so that every user and every item would be represented in the training data. For the description of my algorithm I assume there are more users than items. If this is not true for you then just swap the two. Here's my algorithm.
Select one observation for each user
For each item that has only one observation and has not already been selected from the previous step select one observation
Merge the results of the previous two steps together.
This should produce a set of observations that covers all of the users and all of the items.
Calculate how many observations you need to fill your training set (generally 80% of the total number of observations)
Calculate how many observations are in the merged set from step 3.
The difference between steps 4 and 5 is the number of remaining observations necessary to fill the training set.
Randomly select enough of the remaining observations to fill the training set.
Merge the sets from step 3 and 6: this is your training set.
The remaining observations is your testing set.
As I mentioned, I'm doing this using Treasure Data and Presto so the only tool I have at my disposal is SQL, common table expressions, temporary tables, and Treasure Data workflow.
You're quite correct in your basic logic: if you have only one observation in a class, you must include that in the training set for the model to have any validity in that class.
However, dividing the input into these classes depends on the interactions among various observations. Can you identify classes of data, such as the "only rating" issue you mentioned? As you find other small classes, you'll also need to ensure that you have enough of those observations in your training data.
Unfortunately, this is a process that's tricky to automate. Most one-time applications simply have to hand-pick those observations from the data, and then distribute the others per normal divisions. This does have a problem that the special cases are over-represented in the training set, which can detract somewhat from the normal cases in training the model.
Do you have the capability of tuning the model as you encounter later data? This is generally the best way to handle sparse classes of input.
collaborative filtering (matrix factorization) can't have a good recommendation for an unseen user with no feedback. Nevertheless, an evaluation should consider this case and take it into account.
One thing you can do is to report performance for all test users, just test users with some feedback and just unseen users with no feedback.
So I'd say keep the test, train split random but evaluate separately for unseen users.
More info here.
I'm trying to classify some data using knime with knime-labs deep learning plugin.
I have about 16.000 products in my DB, but I have about 700 of then that I know its category.
I'm trying to classify as much as possible using some DM (data mining) technique. I've downloaded some plugins to knime, now I have some deep learning tools as some text tools.
Here is my workflow, I'll use it to explain what I'm doing:
I'm transforming the product name into vector, than applying into it.
After I train a DL4J learner with DeepMLP. (I'm not really understand it all, it was the one that I thought I got the best results). Than I try to apply the model in the same data set.
I thought I would get the result with the predicted classes. But I'm getting a column with output_activations that looks that gets a pair of doubles. when sorting this column I get some related date close to each other. But I was expecting to get the classes.
Here is a print of the result table, here you can see the output with the input.
In columns selection it's getting just the converted_document and selected des_categoria as Label Column (learning node config). And in Predictor node I checked the "Append SoftMax Predicted Label?"
The nom_produto is the text column that I'm trying to use to predict the des_categoria column that it the product category.
I'm really newbie about DM and DL. If you could get me some help to solve what I'm trying to do would be awesome. Also be free to suggest some learning material about what attempting to achieve
PS: I also tried to apply it into the unclassified data (17,000 products), but I got the same result.
I won't answer with a workflow on this one because it is not going to be a simple one. However, be sure to find the text mining example on the KNIME server, i.e. the one that makes use of the bag of words approach.
The task
Product mapping to categories should be a straight-forward data mining task because the information that explains the target variable is available in a quasi-exhaustive manner. Depending on the number of categories to train though, there is a risk that you might need more than 700 instances to learn from.
Some resources
Here are some resources, only the first one being truly specialised in text mining:
Introduction on Information Retrieval, in particular chapter 13;
Data Science for Business is an excellent introduction to data mining, including text mining (chapter 10), also do not forget the chapter about similarity (chapter 6);
Machine Learning with R has the advantage of being accessible enough (chapter 4 provides an example of text classification with R code).
Preprocessing
First, you will have to preprocess your product labels a bit. Use KNIME's text analytics preprocessing nodes for that purpose, that is after you've transformed the product labels with Strings to Document:
Case Convert, Punctuation Erasure and Snowball Stemmer;
you probably won't need Stop Word Filter, however, there may be quasi-stop words such as "product", which you may need to remove manually with Dictionary Filter;
Be careful not to use any of the following without testing testing their impact first: N Chars Filter (g may be a useful word), Number Filter (numbers may indicate quantities, which may be useful for classification).
Should you encounter any trouble with the relevant nodes (e.g. Punctuation Erasure can be tricky amazingly thanks to the tokenizer), you can always apply String Manipulation with regex before converting the Strings to Document.
Keep it short and simple: the lookup table
You could build a lookup table based on the 700 training instances. The book Data mining techniques as well as resource (2) present this approach in some detail. If any model performs any worse than the lookup table, you should abandon the model.
Nearest neighbors
Neural networks are probably overkill for this task.
Start with a K Nearest Neighbor node (applying a string distance such as Cosine, Levensthein or Jaro-Winkler). This approach requires the least amount of data wrangling. At the very least, it will provide an excellent baseline model, so it is most definitely worth a shot.
You'll need to tune the parameter k and to experiment with the distance types. The Parameter Optimization Loop pair will help you with optimizing k, you can include a Cross-Validation meta node inside of the said loop to obtain an estimate of the expected performance given k instead of only one point estimate per value of k. Use Cohen's Kappa as an optimization criterion, as proposed by the resource number (3) and available via the Scorer node.
After the parameter tuning, you'll have to evaluate the relevance of your model using yet another Cross-Validation meta node, then follow up with a Loop pair including Scorer to calculate the descriptives on performance metric(s) per iteration, finally use Statistics. Kappa is a convenient metric for this task because the target variable consists of many product categories.
Don't forget to test its performance against the lookup table.
What next ?
Should lookup table or k-nn work well for you, then there's nothing else to add.
Should any of those approaches fail, you might want to analyse the precise cases on which it fails. In addition, training set size may be too low, so you could manually classify another few hundred or thousand instances.
If after increasing the training set size, you are still dealing with a bad model, you can try the bag of words approach together with a Naive Bayes classifier (see chapter 13 of the Information Retrieval reference). There is no room here to elaborate on the bag of words approach and Naive Bayes but you'll find the resources here above useful for that purpose.
One last note. Personally, I find KNIME's Naive Bayes node to perform poorly, probably because it does not implement Laplace smoothening. However, KNIME's R Learner and R Predictor nodes will allow you to use R's e1071 package, as demonstrated by resource (3).
I was wondering if there is any algorithm for incrementally adding new classes to existing classifier system. For e.g. if I have trained a system with 50 categories, and I want to add another 10 categories to the system, what methods should I look into? There are wide range of algorithms that allow incrementally updating system with additional training samples from existing categories, but I am not aware of methods that will allow adding more categories. Theoretically, I think Nearest Neighbor like algorithms can be applied to this task, but are there other algorithms that are suitable for large scale tasks (say updating a system trained with 500 categories with 50 additional categories? May be in the domain of incremental decision trees? Algorithms like incremental SVM do not scale very well for large number of categories. If there is any paper/code I would appreciate pointers to it.
If I understand your question correctly, you're asking about divisive clustering (you have a given set of data and want to re-cluster them with a larger number of groups than before).
Most algorithms I'm familiar with would require re-building the clustering basically from scratch. However, you might want to look at the BIRCH algorithm. Since it stores only a summary of the classes (without explicit data references), it is a) suitable for Big Data™, and b) it features a kind of distance measure that might tell you which category you should split next (in case you want to dynamically generate additional 50 "most distinct" categories).
I'm working on a project where I need to predict future stats based on past stats of basketball players. I would like to be able to predict next season's statistics based on the statistics of the past three seasons (if there are three previous seasons to choose from). Does anyone have a suggestion for a good prediction algorithm I could use? The data is continuous and there can be anywhere between 5-14 dimensions (age, minutes, points, etc.)
Thanks!
Note: I'd really like to use the program Weka to do this.
Out of the box, random forest would likely give you a strong baseline, so I would start with this.
You can also try try linear regression, which is a simple yet relative effective method, but depending on the data might require a bit more tweaking (for example transforming some of the input and/or out variables).
Gradient boosting regression is another strong predictor, but typically also needs more tweaking to work well.
All of these algorithms have Weka implementations.
There obviously isn't one correct answer, but for anyone looking to do something similar, I'll better describe my problem and the solution that I've found. I created a csv file where each row is a different season, and each column contains a different attribute. For each attribute that I would like to predict, I have the stats for the current season and then another column for the stats for the previous season. The first (rookie) season will have 0 for all 'previous season' columns. With this data set, I loaded it into Weka and used a Multilayer Perceptron with the test-option set to Cross-Validation. I set the number of folds to somewhere between 80-90% of the number of seasons available.
Finally, to predict the next season's statistics, you add one more row to the end and input the last-season values with "?" in the columns that you would like to predict. If anyone would like a deeper example, I'd be glad to provide one.
I think also if you truly want to create an accurate prediction you have to look at player movement and if a player moves to a team with a losing record, do they increase their minutes to have a larger role which would inflate stats or move to a winning team for a lesser role where they could see a decrease in stats.