My task here is to find a way to get a suggested value of the most important feature or features. By changing into the suggested values of the features, I want the classification result to change as well.
Snapshot of dataset
The following is the procedures that I have tried so far:
Import dataset (shape: 1162 by 22)
Build a simple neural network (2 hidden layers)
Since the dependent variable is simply either 0 or 1 (classification problem), I onehot-encoded the variable. So it's either [0, 1] or [1,0]
After splitting into train & test data, I train my NN model and got accuracy of 77.8%
To know which feature (out of 21) is the most important one in the determination of either 0 or 1, I trained the data using Random Forest classifier (scikit-learn) and also got 77.8% accuracy and then used the 'feature_importances_' offered by the random forest classifier.
As a result, I found out that a feature named 'a_L4' ranks the highest in terms of relative feature importance.
The feature 'a_L4' is allowed to have a value from 0 to 360 since it means an angle. In the original dataset, 'a_L4' comprises of only 12 values that are [5, 50, 95, 120, 140, 160, 185, 230, 235, 275, 320, 345].
I augmented the original dataset by directly adding all the possible 12 values for each cases giving a new dataset of shape (1162x12 by 22).
I imported the augmented dataset and tested it on the previously trained NN model. The result was a FAILURE. There hardly was any change in the classification meaning almost no '1's switched to '0's.
My conclusion was that changing the values of 'a_L4' was not enough to bring a change in the classification. So I additionally did the same procedure again for the 2nd most important feature which in this case was 'b_L7_p1'.
So writing all the possible values that the two most important features can have, now the new dataset becomes the shape of (1162x12x6 by 22). 'b_L7_p1' is allowed to have 6 different values only, thus the multiplication by 6.
Again the result was a FAILURE.
So, my question is what might have I done wrong in the procedure described above? Do I need to keep searching for more important features and augment the data with all the possible values they can have? But since this is a tedious task with multiple procedures to be done manually and leads to a dataset with a huge size, I wish there was a way to construct an inference-based NN model that can directly give out the suggested values of a certain feature or features.
I am relatively new to this field of research, so could anyone please tell me some key words that I should search for? I cannot find any work or papers regarding this issue on Google.
Thanks in advance.
In this case I would approach the problem in the following way:
Normalize the whole dataset. As you can see from the dataset your features have different scales. It is utterly important that you make all features to have the same scale. Have a look at: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
The second this that I would do now is train and evaluate a model (It can be whatever you want) to get a so called baseline model.
Then, I would try PCA to see whether all features are needed. Maybe you are including unnecessary sparsity to the model. See: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
For example if you set the n_components in PCA to be 0.99 then you are reducing the number of features while retaining as 0.99 explained variance.
Then I would train the model to see whether there is any improvement. Please note that only by adding the normalization itself there should be an improvement.
If I want to see by the dataset itself which features are important I would do: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html This would select a specified number of features based on some statistical test lets say: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html
Train a model and evaluate it again to see whether there is some improvement.
Also, you should be aware that the NNs can perform feature engineering by themselves, so computing feature importance is redundant in a way.
Let me know whether you will see any improvements.
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 started developing some LSTM-models and now have some questions about normalization.
Lets pretend I have some time series data that is roughly ranging between +500 and -500. Would it be more realistic to scale the Data from -1 to 1, or is 0 to 1 a better way, I tested it and 0 to 1 seemed to be faster. Is there a wrong way to do it? Or would it just be slower to learn?
Second question: When do I normalize the data? I split the data into training and testdata, do I have to scale / normalize this data seperately? maybe the trainingdata is only ranging between +300 to -200 and the testdata ranges from +600 to -100. Thats not very good I guess.
But on the other hand... If I scale / normalize the entire dataframe and split it after that, the data is fine for training and test, but how do I handle real new incomming data? The model is trained to scaled data, so I have to scale the new data as well, right? But what if the new Data is 1000? the normalization would turn this into something more then 1, because its a bigger number then everything else before.
To make a long story short, when do I normalize data and what happens to completely new data?
I hope I could make it clear what my problem is :D
Thank you very much!
Would like to know how to handle reality as well tbh...
On a serious note though:
1. How to normalize data
Usually, neural networks benefit from data coming from Gaussian Standard distribution (mean 0 and variance 1).
Techniques like Batch Normalization (simplifying), help neural net to have this trait throughout the whole network, so it's usually beneficial.
There are other approaches that you mentioned, to tell reliably what helps for which problem and specified architecture you just have to check and measure.
2. What about test data?
Mean to subtract and variance to divide each instance by (or any other statistic you gather by any normalization scheme mentioned previously) should be gathered from your training dataset. If you take them from test, you perform data leakage (info about test distribution is incorporated into training) and you may get false impression your algorithm performs better than in reality.
So just compute statistics over training dataset and use them on incoming/validation/test data as well.
I want to predict stock price.
Normally, people would feed the input as a sequence of stock prices.
Then they would feed the output as the same sequence but shifted to the left.
When testing, they would feed the output of the prediction into the next input timestep like this:
I have another idea, which is to fix the sequence length, for example 50 timesteps.
The input and output are exactly the same sequence.
When training, I replace last 3 elements of the input by zero to let the model know that I have no input for those timesteps.
When testing, I would feed the model a sequence of 50 elements. The last 3 are zeros. The predictions I care are the last 3 elements of the output.
Would this work or is there a flaw in this idea?
The main flaw of this idea is that it does not add anything to the model's learning, and it reduces its capacity, as you force your model to learn identity mapping for first 47 steps (50-3). Note, that providing 0 as inputs is equivalent of not providing input for an RNN, as zero input, after multiplying by a weight matrix is still zero, so the only source of information is bias and output from previous timestep - both are already there in the original formulation. Now second addon, where we have output for first 47 steps - there is nothing to be gained by learning the identity mapping, yet network will have to "pay the price" for it - it will need to use weights to encode this mapping in order not to be penalised.
So in short - yes, your idea will work, but it is nearly impossible to get better results this way as compared to the original approach (as you do not provide any new information, do not really modify learning dynamics, yet you limit capacity by requesting identity mapping to be learned per-step; especially that it is an extremely easy thing to learn, so gradient descent will discover this relation first, before even trying to "model the future").
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).
I have some data in a pandas dataframe (although pandas is not the point of this question). As an experiment I made column ZR as column Z divided by column R. As a first step using scikit learn I wanted to see if I could predict ZR from the other columns (which should be possible as I just made it from R and Z). My steps have been.
columns=['R','T', 'V', 'X', 'Z']
for c in columns:
results[c] = preprocessing.scale(results[c])
results['ZR'] = preprocessing.scale(results['ZR'])
labels = results["ZR"].values
features = results[columns].values
#print labels
#print features
regr = linear_model.LinearRegression()
regr.fit(features, labels)
print(regr.coef_)
print np.mean((regr.predict(features)-labels)**2)
This gives
[ 0.36472515 -0.79579885 -0.16316067 0.67995378 0.59256197]
0.458552051342
The preprocessing seems wrong as it destroys the Z/R relationship I think. What's the right way to preprocess in this situation?
Is there some way to get near 100% accuracy? Linear regression is the wrong tool as the relationship is not-linear.
The five features are highly correlated in my data. Is non-negative least squares implemented in scikit learn ? ( I can see it mentioned in the mailing list but not the docs.) My aim would be to get as many coefficients set to zero as possible.
You should easily be able to get a decent fit using random forest regression, without any preprocessing, since it is a nonlinear method:
model = RandomForestRegressor(n_estimators=10, max_features=2)
model.fit(features, labels)
You can play with the parameters to get better performance.
The solutions is not as easy and can be very influenced by your data.
If your variables R and Z are bounded (for ex 0<R<1 -3<Z<2) then you should be able to get a good estimation of the output variable using neural network.
Using neural network you should be able to estimate your output even without preprocessing the data and using all the variables as input.
(Of course here you will have to solve a minimization problem).
Sklearn do not implement neural network so you should use pybrain or fann.
If you want to preprocess the data in order to make the minimization problem easier you can try to extract the right features from the predictor matrix.
I do not think there are a lot of tools for non linear features selection. I would try to estimate the important variables from you dataset using in this order :
1-lasso
2- sparse PCA
3- decision tree (you can actually use them for features selection ) but I would avoid this as much as possible
If this is a toy problem I would sugges you to move towards something of more standard.
You can find a lot of examples on google.