i'm working on my thesis and i used a Catboost classifier to perform a binary analysis on a very unbalanced dataset:
class0 = x number of samples
class1 = 10*x number of samples
In order to optimize the performance of the model i changed the weights of the classes, giving an higher weight to the minority class, and then i performed a grid search cross validation in which it is searched the set of hyperparameters that reduces the crossentropy loss associated to the catboost model.
At this point i also changed the classificaiton threshold by maximizing the G-mean metric (sqruare root of sensitivity multiplied by specificity).
In you opinion, if you are experts or informed about ensemble methods of type boosting, is it right to procede in this way to increase the performance of the model when the dataset is unbalanced? Maybe it would be enough just to change the weights and use the grid search instead of changing also the classification threshold?
Thank you in advance!
Related
Consider a dataset A which has examples for training in a binary classification problem. I have used SVM and applied the weighted method (in MATLAB) since the dataset is highly imbalanced. I have applied weights as inversely proportional to the frequency of data in each class. This is done on training using the command
fitcsvm(trainA, trainTarg , ...
'KernelFunction', 'RBF', 'KernelScale', 'auto', ...
'BoxConstraint', C,'Weight',weightTrain );
I have used 10 folds cross-validation for training and learned the hyperparameter as well. so, inside CV the dataset A is split into train (trainA) and validation sets (valA). After training is over and outside the CV loop, I get the confusion matrix on A:
80025 1
0 140
where the first row is for the majority class and the second row is for the minority class. There is only 1 false positive (FP) and all minority class examples have been correctly classified giving true positive (TP) = 140.
PROBLEM: Then, I run the trained model on a new unseen test data set B which was never seen during training. This is the confusion matrix for testing on B .
50075 0
100 0
As can be seen, the minority class has not been classified at all, hence the purpose of weights has failed. Although, there is no FP the SVM fails to capture the minority class examples.
I have not applied any weights or balancing method such as sampling (SMOTE, RUSBoost etc) on B. What could be wrong and how to overcome this problem?
Class misclassification weights could be set instead of sample weights!
You can set the class weights based on the following example.
Mis-classification weight for class A(n-records; dominant) into class B (m-records; minority class) can be n/m.
Mis-classification weight For class B as class A can be set as 1 or m/n based on the severity, which you want to impose on the learning
c=[0 2.2;1 0];
mod=fitcsvm(X,Y,'Cost',c)
According to documentation:
For two-class learning, if you specify a cost matrix, then the
software updates the prior probabilities by incorporating the
penalties described in the cost matrix. Consequently, the cost matrix
resets to the default. For more details on the relationships and
algorithmic behavior of BoxConstraint, Cost, Prior, Standardize, and
Weights, see Algorithms.
Area Under Curve (AUC) is usually used to measure performance of models that applied on unbalanced data. It is also good to plot ROC curve to visually get more insights. Using only confusion matrix for such models may lead to misinterpretation.
perfcurve from the Statistics and Machine Learning Toolbox provides both functionalities.
I am solving a classification problem. I train my unsupervised neural network for a set of entities (using skip-gram architecture).
The way I evaluate is to search k nearest neighbours for each point in validation data, from training data. I take weighted sum (weights based on distance) of labels of nearest neighbours and use that score of each point of validation data.
Observation - As I increase the number of epochs (model1 - 600 epochs, model 2- 1400 epochs and model 3 - 2000 epochs), my AUC improves at smaller values of k but saturates at the similar values.
What could be a possible explanation of this behaviour?
[Reposted from CrossValidated]
To cross check if imbalanced classes are an issue, try fitting a SVM model. If that gives a better classification(possible if your ANN is not very deep) it may be concluded that classes should be balanced first.
Also, try some kernel functions to check if this transformation makes data linearly separable?
Is it possible to train classifiers in sklearn with a cost matrix with different costs for different mistakes? For example in a 2 class problem, the cost matrix would be a 2 by 2 square matrix. For example A_ij = cost of classifying i as j.
The main classifier I am using is a Random Forest.
Thanks.
The cost-sensitive framework you describe is not supported in scikit-learn, in any of the classifiers we have.
You could use a custom scoring function that accepts a matrix of per-class or per-instance costs. Here's an example of a scorer that calculates per-instance misclassification cost:
def financial_loss_scorer(y, y_pred, **kwargs):
import pandas as pd
totals = kwargs['totals']
# Create an indicator - 0 if correct, 1 otherwise
errors = pd.DataFrame((~(y == y_pred)).astype(int).rename('Result'))
# Use the product totals dataset to create results
results = errors.merge(totals, left_index=True, right_index=True, how='inner')
# Calculate per-prediction loss
loss = results.Result * results.SumNetAmount
return loss.sum()
The scorer becomes:
make_scorer(financial_loss_scorer, totals=totals_data, greater_is_better=False)
Where totals_data is a pandas.DataFrame with indexes that match the training set indexes.
You could always just look at your ROC curve. Each point on the ROC curve corresponds to a separate confusion matrix. So by specifying the confusion matrix you want, via choosing your classifier threshold implies some sort of cost weighting scheme. Then you just have to choose the confusion matrix that would imply the cost matrix you are looking for.
On the other hand if you really had your heart set on it, and really want to "train" an algorithm using a cost matrix, you could "sort of" do it in sklearn.
Although it is impossible to directly train an algorithm to be cost sensitive in sklearn you could use a cost matrix sort of setup to tune your hyper-parameters. I've done something similar to this using a genetic algorithm. It really doesn't do a great job, but it should give a modest boost to performance.
One way to circumvent this limitation is to use under or oversampling. E.g., if you are doing binary classification with an imbalanced dataset, and want to make errors on the minority class more costly, you could oversample it. You may want to have a look at imbalanced-learn which is a package from scikit-learn-contrib.
May not be direct to your question (since you are asking about Random Forest).
But for SVM (in Sklearn), you can utilize the class_weight parameter to specify the weights of different classes. Essentially, you will pass in a dictionary.
You might want to refer to this page to see an example of using class_weight.
I am using knn to do classification for a telecom problem. I splitted my data into 70% training and 30% validation. While the knn classifier is able to catch over 80% in 2 deciles in training, its performance in validation sample is as good as random 45 degree line. I am surprised how does KNN work that the model performance in training and validation are so different.
Any pointers ?
Reasonable pointers are hardly possible without more details. The behavior of your KNN depends on several aspects:
The parameter K defining the neighbors. If it is set to K=1, for example, you will get no training error at all, this showing that the consideration of training-to-validation-error may not be justified.
The parameter K is often found using cross validation. I would suggest you to do this as well.
The distance metric. Which function are you using, are there different units, length scales, etc.?
The noise of your data, the size of your data ... -- there simply exist data sets which are hard to describe.
By the way: can you tell what kind of data you want to describe, and, if possible, also provide some examples or show some scatter plot (data and your result)?
I am new to using Matlab and am trying to follow the example in the Bioinformatics Toolbox documentation (SVM Classification with Cross Validation) to handle a classification problem.
However, I am not able to understand Step 9, which says:
Set up a function that takes an input z=[rbf_sigma,boxconstraint], and returns the cross-validation value of exp(z).
The reason to take exp(z) is twofold:
rbf_sigma and boxconstraint must be positive.
You should look at points spaced approximately exponentially apart.
This function handle computes the cross validation at parameters
exp([rbf_sigma,boxconstraint]):
minfn = #(z)crossval('mcr',cdata,grp,'Predfun', ...
#(xtrain,ytrain,xtest)crossfun(xtrain,ytrain,...
xtest,exp(z(1)),exp(z(2))),'partition',c);
What is the function that I should be implementing here? Is it exp or minfn? I will appreciate if you can give me the code for this section. Thanks.
I will like to know what does it mean when it says exp([rbf_sigma,boxconstraint])
rbf_sigma: The svm is using a gaussian kernel, the rbf_sigma set the standard deviation (~size) of the kernel. To understand how kernels work, the SVM is putting the kernel around every sample (so that you have a gaussian around every sample). Then the kernels are added up (sumed) for the samples of each category/type. At each point the type which sum is higher would be the "winner". For example if type A has a higher sum of these kernels at point X, then if you have a new datum to classify in point X, it will be classified as type A. (there are other configuration parameters that may change the actual threshold where a category is selected over another)
Fig. Analyze this figure from the webpage you gave us. You can see how by adding up the gaussian kernels on the red samples "sumA", and on the green samples "sumB"; it is logical that sumA>sumB in the center part of the figure. It is also logical that sumB>sumA in the outer part of the image.
boxconstraint: it is a cost/penalty over miss-classified data. During the training stage of the classifier, where you use the training data to adjust the SVM parameters, the training algorithm is using an error function to decide how to optimize the SVM parameters in an iterative fashion. The cost for a miss-classified sample is proportional to how far it is from the boundary where it would have been classified correctly. In the figure that I am attaching the boundary is the inner blue circumference.
Taking into account BGreene indications and from what I understand of the tutorial:
In the tutorial they advice to try values for rbf_sigma and boxconstraint that are exponentially apart. This means that you should compare values like {0.2, 2, 20, ...} (note that this is {2*10^(i-2), i=1,2,3,...}), and NOT like {0.2, 0.3, 0.4, 0.5} (which would be linearly apart). They advice this to try a wide range of values first. You can further optimize later FROM the first optimum that you obtained before.
The command "[searchmin fval] = fminsearch(minfn,randn(2,1),opts)" will give you back the optimum values for rbf_sigma and boxconstraint. Probably you have to use exp(z) because it affects how fminsearch increments the values of z(1) and z(2) during the search for the optimum value. I suppose that when you put exp(z(1)) in the definition of #minfn, then fminsearch will take 'exponentially' big steps.
In machine learning, always try to understand that there are three subsets in your data: training data, cross-validation data, and test data. The training set is used to optimize the parameters of the SVM classifier for EACH value of rbf_sigma and boxconstraint. Then the cross validation set is used to select the optimum value of the parameters rbf_sigma and boxconstraint. And finally the test data is used to obtain an idea of the performance of your classifier (the efficiency of the classifier is determined upon the test set).
So, if you start with 10000 samples you may divide the data for example as training(50%), cross-validation(25%), test(25%). So that you will sample randomly 5000 samples for the training set, then 2500 samples from the 5000 remaining samples for the cross-validation set, and the rest of samples (that is 2500) would be separated for the test set.
I hope that I could clarify your doubts. By the way, if you are interested in the optimization of the parameters of classifiers and machine learning algorithms I strongly suggest that you follow this free course -> www.ml-class.org (it is awesome, really).
You need to implement a function called crossfun (see example).
The function handle minfn is passed to fminsearch to be minimized.
exp([rbf_sigma,boxconstraint]) is the quantity being optimized to minimize classification error.
There are a number of functions nested within this function handle:
- crossval is producing the classification error based on cross validation using partition c
- crossfun - classifies data using an SVM
- fminsearch - optimizes SVM hyperparameters to minimize classification error
Hope this helps