I am using the bank dataset for cluster analysis in Weka.
The dataset is here: bank-k.arff
However, the dataset appears to be not ideal for cluster analysis. When using algorithms like EM, k-means, and cobweb, the result does not show trends. Classes to cluster evaluations yield unhelpful results. For instance, when using the region as the class:
bank cluster weka
Most of the graphs are like this, which does not yield helpful results.
In contrast, the iris dataset does return distinguishing results:
Iris in Weka
Am I doing it incorrectly? I am new in this field of study. Please give some advice if possible.
Think you in advance!
Related
I'm developing a regression model. But I ran into a problem when preparing the data. 17 out of 20 signs are categorical, and there are a lot of categories in each of them. Using one-hot-encoding, my data table is transformed into a 10000x6000 table. How should I prepare this type of data?
I used PCA, trying to reduce the dimension, but even 70% of the variance is in 2500 features. That's why I joined.
Unfortunately, I can't attach the dataset, as it is confidential
How do I prepare the data to achieve the best results in the learning process?
Can the data be mapped more accurately in a non-linear manner? If so, you might want to try using an autoencoder for dimensionality reduction.
One thing to note about PCA is that it computes an orthogonal projection of the data into linear space. This means that it only gives a linear mapping of the data. Autoencoders, on the other hand, can give you a non-linear mapping, and so is able to represent a greater amount of variance in the data in fewer dimensions. Just be sure to use non-linear activation functions in your autoencoder architecture.
It really depends on exactly what you are trying to do. Getting a covariance matrix (and also PCA decomp.) will give you great insight about which classes tend to come together (and this requires one-hot encoded categories), but training a model off of that might be problematic.
In general, it really depends on the model you want to use.
One option would be a random forest. They can definitely be used for regression, though they need to be trained specifically for that. SKLearn has a class just for this:
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
The benifits of random forest is that it is great for tabular data (as is the case here), and can easily be trained using numerical values for class features, meaning your data vector can only be of dimension 20!
Decision tree models (such as random forest) are being shown to out-preform deep-learning in many cases, and this may be one of them.
TLDR; If you use a random forest, it can take learn even with numerical values for categories, and you can avoid creating incredibly large vectors for data.
My original data contains some missing values and I used multiple imputation to fill them. My next objective is to use these data in SVM and ANN. I originally thought MI would give me a "pooled" completed dataset but it turned out that MI only gives pooled analysis results regarding the imputed datasets. So my questions are:
1) Is there any way, like any equation, I can use to aggregate the imputed datasets into one dataset and use it for further analysis;
2) If not, how should proceed my study using the multiple datasets.
Thank you!
This is a general misunderstanding about MI.
The general process is supposed to be like this:
Multiple Imputation
Analysis for each imputed dataset
Pooling
If you would do the imputation and then merge all imputed dataset to one imputed dataset you loose all the benefit of MI. Then you could have just used any other imputation method. The idea is to perform your analysis for example 5 times, one time for each imputed dataset. Because you want to account for the different outcomes your analysis could have had with different imputed input datasets. Afterwards you pool / merge the results of your analysis.
The whole process is not so common in ML. But in your case you could for example use SVM on all 5 datasets and then afterwards compare the results / come up with a procedure to merge/combine the results.
I want to develop a framework(for QA testing purpose) that validates a machine learning model. I had a lot of discussions with my peers and read articles from the google.
Most of the discussions or articles are telling machine learning model will evolve with the test data that we provide. correct me if I'm wrong.
What is the possibility of developing a framework that validates the machine learning model will give accurate results?
Few ways to test the model from the articles I read: Split and Multi-split technique, Metamorphic testing
Please also suggest any other approaches
QA testing of ML-based software requires additional, and rather unconventional, tests because oftentimes their outputs for a given set of inputs are not defined, deterministic, or known a priori and they produce approximations rather than exact results.
QA may be designed to test against:
naive but predictable benchmark methods: the average method in forecasting, the class-frequency-based classifier in classification, etc.
sanity checks (the outputs being feasible/rational): e.g., is the predicted age positive?
preset objective acceptance levels: e.g., is its AUCROC > 0.5?
extreme/boundary cases: e.g., thunderstorm conditions for a weather forecast model.
bias-variance tradeoff: what is its performance on in-sample and out-of-sample data? K-Fold cross-validation is useful here.
the model itself: is the coefficient of variation of its performance measure (e.g., AUCROC) from n runs on the same data for same/random train and test partitioning within a reasonable bound?
Some of these tests need performance measures. Here is a comprehensive library of them.
I think the data flow is, actually, the one that needs to be tested here such as raw input, manipulation, test output and predictions. For example, if you have a simple linear model you actually want to test the predictions produced from that model instead of the coefficients of the model. So, maybe, the high level steps are summarized as below;
Raw Input: Does the raw input make sense? Before you start manipulating, you need to be sure the raw data values are within the expected limits. For example, if you normally see 5-10% NA rate in some data, having 95% NA rate in a new batch might be an indicator that something is wrong.
Train/Predict Ready Input: Either you train a new model or feeding new data into a already trained model for prediction, you probably want to be sure that manipulated data makes sense, too. Some ML algorithms are delicate to data anomalies. You don't want to predict a credit score around thousands just because you have some data anomalies in the input.
Model Success: By this time, you should have some idea about your model success. So, you can measure the model's performance on a new test data. You can also check train and test score if they are not significantly different (i.e. Overfitting). If you're retraining, you can compare with the previous training scores. Or, you can separate some test set and compare its score.
Predictions: Finally, you need to be sure your final output makes sense before delivering to production/clients. For example, if you're revenue forecasting for a very small shop, the daily revenue predictions can't be million dollars or some negative amounts.
Full disclosure, I wrote a small Python package for this. You can check here or download as below,
pip install mlqa
lets assume we have a huge Database providing us with the training data D and a dedicated smaller testing data T for a machine learning problem.
The data covers many aspects of a real world problem and thus is very diverse in its structure.
When we now train a not closer defined machine learning algorithm (Neural Network, SVM, Random Forest, ...) with D and finally test the created model against T we obtain a certain performance measure P (confusion matrix, mse, ...).
The Question: If I could achieve a better performance, by dividing the problem ito smaller sub-problems, e.g. by clustering D into several distinct training sets D1, D2, D3, ..., how could I find the optimal clusters? (number of clusters, centroids,...)
In a brute-force fashion I am thinking about using a kNN Clustering with a random number of clusters C, which leads to the training data D1, D2,...Dc.
I would now train C different models and finally test them against the training sets T1, T2, ..., Tc, where the same kNN Clustering has been used to split T into the C test sets T1,..,Tc.
The combination which gives me the best overall performance mean(P1,P2,...,Pc) would be the one I would like to choose.
I was just wondering whether you know a more sophisticated way than brute-forcing this?
Many thanks in advance
Clustering is hard.
Much harder than classification, because you don't have labels to tell you if you are doing okay, or not well at all. It can't do magic, but it requires you to carefully choose parameters and evaluate the result.
You cannot just dump your data into k-means and expect anything useful to come out. You'd first need to really really carefully clean and preprocess your data, and then you might simply figure out that it actually is only one single large clump...
Furthermore, if clustering worked well and you train classifiers on each cluster independently, then every classifier will miss crucial data. The result will likely performing really really bad!
If you want to only train on parts of the data, use a random forest.
But it sounds like you are more interested in a hierarchical classification approach. That may work, if you have good hierarchy information. You'd first train a classifier on the category, then another within the category only to get the final class.
I use Weka to test machine learning algorithms on my dataset. I have 3800 rows and around 25 features. I am testing the combination of different features for prediction models and seem to predict lower than just the oneR algorithm does with the use of Cross-validation. Even C4.5 does not predict better, sometimes it does and sometimes it does not on basis of the features that are still able to classify.
But, on a certain moment I splitted my dataset in a testset and dataset(20/80), and testing it on the testset, the C4.5 algorithm had a far higher accuracy than my OneR algorithm had. I thought, with the small size of the dataset, it probably is just a coincidence that it predicted very well(the target was still splitted up relatively as target attributes). And therefore, its more useful to use Cross-validation on small datasets like these.
However, testing it on another testset, did give the high accuracy towards the testset using C4.5. So, my question actually is, what is the best way to test datasets when the datasets are actually pretty small?
I saw some posts where it is discussed, but I am still not sure what is the right way to do it.
It's almost always a good approach to test your model via Cross-Validation.
A rule of thumb is to use 10 fold cross validation.
In your case, 10 fold cross validation will do the following in Weka:
split your 3800 training instances into 10 sets of 380 instances
for each set (s = 1 .. 10) :
use the instances from s for testing and the other 9 sets for training a model (3420 training instances)
the result will be an average of the results obtained with the 10 models used.
Try to avoid testing your dataset using the training set option, because that could result in creating a model that works very well for you existing data but could have big problems with other new instances (overfitting).