I would like to perform feature analysis in WEKA. I have a data set of 8 features and 65 instances.
I would like to perform feature selection and optimization functionalities that are available for machine learning methods like SVM.
For example in Weka I would like to know how I can display which of the features contribute best to the classification result.
I think that WEKA provides a nice graphical user interface and allows a very detailed analysis of the influence of single features. But I dont know how to use it. Any help?
You have two options:
You can perform attribute selection using filters. For instance you can use the AttributeSelection tab (or filter) with the search method Ranker and the attribute evaluation metric InfoGainAttributeEval. This way you get a ranked list of the most predictive features according to its Information Gain score. I have done this many times with good results. Sometimes it helps even to increase the accuracy of SVMs, which are known not to need (too much) of feature selection. You can try with other search methods in order to find subgroups of coupled predictors, and with other metrics.
You can just look at the coefficients in the SVM output. For instance, in linear SVMs, the classifier is a polynomial like a1.f1 + a2.f2 + ... + an.fn + fn+1 > 0, being ai the attribute values for an instance, and fi the "weights" obtained in the SVM training algorithm. In consequence, those weights with values close to 0 represent attributes that do not count too much, thus being bad predictors; extreme weights (either positive or negative) represent good predictors.
Additionally, you can check the visualization options available for a particular classifier (e.g. J48 is a decision tree, the attribute used in the root test is for the best predictor). You can check the AttributeSelection tab visualization options as well.
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.
I am given a data that consists of N sequences of variable lengths of hidden variables and their corresponding observed variables (i.e., I have both the hidden variables and the observed variables for each sequence).
Is there a way to find the order K of the "best" HMM model for this data, without exhaustive search? (justified heuristics are also legitimate).
I think there may be a confusion about the word "order":
A first-order HMM is an HMM which transition matrix depends only on the previous state. A 2nd-order HMM is an HMM which transition matrix depends only on the 2 previous states, and so on. As the order increases, the theory gets "thicker" (i.e., the equations) and very few implementations of such complex models are implemented in mainstream libraries.
A search on your favorite browser with the keywords "second-order HMM" will bring you to meaningful readings about these models.
If by order you mean the number of states, and with the assumptions that you use single distributions assigned to each state (i.e., you do not use HMMs with mixtures of distributions) then, indeed the only hyperparameter you need to tune is the number of states.
You can estimate the optimal number of states using criteria such as the Bayesian Information Criterion, the Akaike Information Criterion, or the Minimum Message Length Criterion which are based on model's likelihood computations. Usually, the use of these criteria necessitates training multiple models in order to be able to compute some meaningful likelihood results to compare.
If you just want to get a blur idea of a good K value that may not be optimal, a k-means clustering combined with the percentage of variance explained can do the trick: if X clusters explain more than, let say, 90% of the variance of the observations in your training set then, going with an X-state HMM is a good start. The 3 first criteria are interesting because they include a penalty term that goes with the number of parameters of the model and can therefore prevent some overfitting.
These criteria can also be applied when one uses mixture-based HMMs, in which case there are more hyperparameters to tune (i.e., the number of states and the number of component of the mixture models).
I'm trying to build a regression based M/L model using tensorflow.
I am trying to estimate an object's ETA based on the following:
distance from target
distance from target (X component)
distance from target (Y component)
speed
The object travels on specific journeys. This could be represented as from A->B or from A->C or from D->F (POINT 1 -> POINT 2). There are 500 specific journeys (between a set of points).
These journeys aren't completely straight lines, and every journey is different (ie. the shape of the route taken).
I have two ways of getting around this problem:
I can have 500 different models with 4 features and one label(the training ETA data).
I can have 1 model with 5 features and one label.
My dilemma is that if I use option 1, that's added complexity, but will be more accurate as every model will be specific to each journey.
If I use option 2, the model will be pretty simple, but I don't know if it would work properly. The new feature that I would add are originCode+ destinationCode. Unfortunately these are not quantifiable in order to make any numerical sense or pattern - they're just text that define the journey (journey A->B, and the feature would be 'AB').
Is there some way that I can use one model, and categorize the features so that one feature is just a 'grouping' feature (in order separate the training data with respect to the journey.
In ML, I believe that option 2 is generally the better option. We prefer general models rather than tailoring many models to specific tasks, as that gets dangerously close to hardcoding, which is what we're trying to get away from by using ML!
I think that, depending on the training data you have available, and the model size, a one-hot vector could be used to describe the starting/end points for the model. Eg, say we have 5 points (ABCDE), and we are going from position B to position C, this could be represented by the vector:
0100000100
as in, the first five values correspond to the origin spot whereas the second five are the destination. It is also possible to combine these if you want to reduce your input feature space to:
01100
There are other things to consider, as Scott has said in the comments:
How much data do you have? Maybe the feature space will be too big this way, I can't be sure. If you have enough data, then the model will intuitively learn the general distances (not actually, but intrinsically in the data) between datapoints.
If you have enough data, you might even be able to accurately predict between two points you don't have data for!
If it does come down to not having enough data, then finding representative features of the journey will come into use, ie. length of journey, shape of the journey, elevation travelled etc. Also a metric for distance travelled from the origin could be useful.
Best of luck!
I would be inclined to lean toward individual models. This is because, for a given position along a given route and a constant speed, the ETA is a deterministic function of time. If one moves monotonically closer to the target along the route, it is also a deterministic function of distance to target. Thus, there is no information to transfer from one route to the next, i.e. "lumping" their parameters offers no a priori benefit. This is assuming, of course, that you have several "trips" worth of data along each route (i.e. (distance, speed) collected once per minute, or some such). If you have only, say, one datum per route then lumping the parameters is a must. However, in such a low-data scenario, I believe that including a dummy variable for "which route" would ultimately be fruitless, since that would introduce a number of parameters that rivals the size of your dataset.
As a side note, NEITHER of the models you describe could handle new routes. I would be inclined to build an individual model per route, data quantity permitting, and a single model neglecting the route identity entirely just for handling new routes, until sufficient data is available to build a model for that route.
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
I am being asked to take a look at a scenario where a company has many projects that they wish to complete, but with any company budget comes into play. There is a Y value of a predefined score, with multiple X inputs. There are also 3 main constraints of Capital Costs, Expense Cost and Time for Completion in Months.
The ask is could an algorithmic approach be used to optimize which projects should be done for the year given the 3 constraints. The approach also should give different results if the constraint values change. The suggested method is multiple regression. Though I have looked into different approaches in detail. I would like to ask the wider community, if anyone has dealt with a similar problem, and what approaches have you used.
Fisrt thing we should understood, a conclution of something is not base on one argument.
this is from communication theory, that every human make a frame of knowledge (understanding conclution), where the frame construct from many piece of knowledge / information).
the concequence is we cannot use single linear regression in math to create a ML / DL system.
at least we should use two different variabel to make a sub conclution. if we push to use single variable with use linear regression (y=mx+c). it's similar to push computer predict something with low accuration. what ever optimization method that you pick...it's still low accuracy..., why...because linear regresion if you use in real life, it similar with predict 'habbit' base on data, not calculating the real condition.
that's means...., we should use multiple linear regression (y=m1x1+m2x2+ ... + c) to calculate anything in order to make computer understood / have conclution / create model of regression. but, not so simple like it. because of computer try to make a conclution from data that have multiple character / varians ... you must classified the data and the conclution.
for an example, try to make computer understood phitagoras.
we know that phitagoras formula is c=((a^2)+(b^2))^(1/2), and we want our computer can make prediction the phitagoras side (c) from two input values (a and b). so to do that, we should make a model or a mutiple linear regresion formula of phitagoras.
step 1 of course we should make a multi character data of phitagoras.
this is an example
a b c
3 4 5
8 6 10
3 14 etc..., try put 10 until 20 data
try to make a conclution of regression formula with multiple regression to predic the c base on a and b values.
you will found that some data have high accuration (higher than 98%) for some value and some value is not to accurate (under 90%). example a=3 and b=14 or b=15, will give low accuration result (under 90%).
so you must make and optimization....but how to do it...
I know many method to optimize, but i found in manual way, if I exclude the data that giving low accuracy result and put them in different group then, recalculate again to the data group that excluded, i will get more significant result. do again...until you reach the accuracy target that you want.
each group data, that have a new regression, is a new class.
means i will have several multiple regression base on data that i input (the regression come from each group of data / class) and the accuracy is really high, 99% - 99.99%.
and with the several class, the regresion have a fuction as a 'label' of the class, this is what happens in the backgroud of the automation computation. but with many module, the user of the module, feel put 'string' object as label, but the truth is, the string object binding to a regresion that constructed as label.
with some conditional parameter you can get the good ML with minimum number of data train.
try it on excel / libreoffice before step more further...
try to follow the tutorial from this video
and implement it in simple data that easy to construct in excel, like pythagoras.
so the answer is yes...the multiple regression is the best approach for optimization.