I am relatively new to the data mining area and have been experimenting with Weka.
I have a dataset which consists of almost 8000 records related to customers and items they have purchased. 58% of this data set has missing values for the "Gender" attribute.
I want to find the missing gender values based on the other data I do have.
I first thought I could do this using a classifier algorithm in Weka using a training set to build a model. Based on examples I saw online, I tried this with pretty much all the available algorithms available in Weka using a training set that consisted of 60-80% of the data which did not have missing values. This gave me a lower accuracy rate than I wanted (80-86% depending on the algorithm used)
Did I go about this correctly? Is there a way to improve this accuracy? I experimented with using different attributes, different pre-processing of the data etc.
I also tried using the ReplaceMissingValues filter on the complete dataset to see how that would handle the missing values. However, it just changed all the missing values to "Female" which obviously cannot be the case. So I'm wondering also wondering if I need to use this filter in my situation or not.
It sounds like you went about it in the correct way. The ReplaceMissingValues filter replaces the missing values with the most frequent of the non-missing values I think, so it is not what you want in this case.
A better way to get an idea of the true accuracy of your gender-predictor would be to use cross-validation instead of the training/test split (Weka has a separate option for that). 80-86% may seem low, but keep in mind that random guessing will only get you about 50%, so it's still a lot better than that. To try to get better performance, pick a classifier that performs well and then play with its parameters until you get better performance. This is likely to be quite labour-intensive (although you could of course use automated methods for tuning, see e.g. Auto-WEKA), but the only way to improve the performance.
You can also combine the algorithm you choose with a separate feature selection step (Weka has a special meta-classifier for this). This may improve performance, but again you'll have to experiment to find the particular configuration that works for you.
Related
Is it possible in Weka to train a model minimizing a cost factor?
I have a data set containing a cost factor in each sample. It defines what using this sample would cost. Now, I would like to select as much of the samples as possible while minimizing this cost factor.
E.g. with Multilayer perceptron, I want to train the neurons in a way, that it chooses as many samples as possible while minimizing the sum of the cost factor.
I've checked all the model options and also searched the package manager for something like that, but I was unable to find anything. Could someone tell me whether this can be done using Weka?
What you are describing sounds more like an optimization problem rather than a classification or regression problem (for which you would use a Weka classifier).
Weka does have some limited support for optimization through its abstract weka.core.Optimization class (e.g., used internally by weka.classifiers.functions.Logistic). But that requires implementing some methods.
To cast your net wider, you might want to take a look at the following article that describes various optimization techniques:
https://machinelearningmastery.com/tour-of-optimization-algorithms/
I am new to SageMaker. I have a large csv dataset which I would like labelled:
sentence_id
sentence
pre_agreed_label
148392
A sentence
0
383294
Another sentence
1
For each sentence, I would like a) a yes/no binary classification in response to a question, and b) on a scale of 1-3, how obvious the classification was. I need the sentence id to map to other parts of the dataset, and will use the pre-agreed labels to assess accuracy.
I have identified SageMaker GroundTruth labelling jobs as a possible way to do this. Is this the best way? In trying to set it up I have run into a few problems.
The first problem is I can't find a way to display only the sentence column to the labellers, hiding the sentence_id and pre_agreed_labels.
The second is that there is either single labelling or multi labelling, but I would like a way to have two sets of single-selection labels:
Select one for binary classification:
Yes
No
Select one for difficulty of classification:
Easy
Medium
Hard
It seems as though this can be done using custom HTML, but I don't know how to do this - the template it gives you doesn't even render
Finally, having not used mechanical turk before, are there ways of ensuring people take the work seriously and don't just select random answers? I can see there's an option to have x number of people answer the same question, but is there also a way to put in an obvious question to which we already have a 'pre_agreed_label' every nth question, and kick people off the task if they get it wrong? There also appears to be a maximum of $1.20 per task which seems odd.
Is there a way to incorporate the uncertainties on my data set into the result of the Savitzky Golay fit? Since I am not passing this information into the function, I asume that it is simply calcuating the 'best fit' via an unweighted least-squares process. I am currently working with data that has non-uniform uncertainty, and so the fit of the data could be improved by including the errors that I have for my main dataset.
The wikipedia page for the Savitzky-Golay filter suggests how I might go about alter the process of calculating the coefficients of the fit, and I am staring at the code for scipy.signal.savgol_filter, but I cannot get my head around what I need to adjust so that this will do what I want it to.
Are there any ready-made weighted SG filters floating about? I find it hard to believe that no-one else has ever needed this tool in Python, but maybe I have missed something.
Check out this Python module: https://github.com/surhudm/savitzky_golay_with_errors
This python script improves upon the traditional Savitzky-Golay filter
by accounting for errors or covariance in the data. The inputs and
arguments are all modelled after scipy.signal.savgol_filter
Matlab function sgolayfilt supports weights. Check the documentation.
I am learning how to do data mining and I am using this data set from UCI's website.
http://archive.ics.uci.edu/ml/datasets/Forest+Fires
The problem I am encountering is how to deal with the area class. My understanding from the description is that I need to apply ln(x+1) to area using AddExpression.
Am I going in the correct direction with this? Or are there other filters I should investigate? Thank you.
I try to answer your question based on the little information you provide. And I haven't worked with the forest-fires data set, but by inspection I see that the classifier attribute "area" often has the value 0. Maybe you can't simply filter out these rows with Area = 0. Your dataset might become too small, or whatnot.
I think you are asked to perform regression of some attribute(s) against "log(area)" in order to linearize it. However,when you try to calculate the log of the Area, values such as log(0) are a problem. values between 0 and 1 might also be problematic.
So a common fix is to add 1 to the value of "Area". This introduces a systematic error, but it is small, and it removes all 0-values, and you can still derive useful models from your log(x+1)-transformed dataset.
And yes, in Weka you do this by "Preprocess"/ AddExpression(x+1). This creates a new attribute. Then you might remove the old area attribute.
Of course, in interpreting your model, you should be aware of the transformation. If you just want to find out what the significant independent attributes are in your linear regression model, I'd say the transformation does not matter. The data points are just shifted a little bit.
What is the meaning of words to keep attribute in Weka StringToWord filter. Is it better to have higher value or not, for getting real results?
In general, it is a good idea to set the limit as high as possible in order to retain as many words as possible. Words with small frequencies can marginally help the classifiers you induce later.
Keeping too many words may look like a bad idea for a matter of efficiency - the higher the number of attributes, the longer it will take to learn the model. However, you can filter the words to keep the most predictive ones using the AttributeSelection filter with the Ranker function and the InfoGainAttributeEval measure. In fact, you can play with the theshold in the AttrivuteSelection filter in order to keep a relatively small number of very predictive words, with independence of their relative frequency.
Additionally, do not forget to set the flag doNotOperatePerClassBasis to true in order to keep all the words relevant to all classes.