I use the python implementation of XGBoost. One of the objectives is rank:pairwise and it minimizes the pairwise loss (Documentation). However, it does not say anything about the scope of the output. I see numbers between -10 and 10, but can it be in principle -inf to inf?
good question. you may have a look in kaggle competition:
Actually, in Learning to Rank field, we are trying to predict the relative score for each document to a specific query. That is, this is not a regression problem or classification problem. Hence, if a document, attached to a query, gets a negative predict score, it means and only means that it's relatively less relative to the query, when comparing to other document(s), with positive scores.
It gives predicted score for ranking.
However, the scores are valid for ranking only in their own groups.
So we must set the groups for input data.
For esay ranking, refer to my project xgboostExtension
If I understand your questions correctly, you mean the output of the predict function on a model fitted using rank:pairwise.
Predict gives the predicted variable (y_hat).
This is the same for reg:linear / binary:logistic etc. The only difference is that reg:linear builds trees to Min(RMSE(y, y_hat)), while rank:pairwise build trees to Max(Map(Rank(y), Rank(y_hat))). However, output is always y_hat.
Depending on the values of your dependent variables, output can be anything. But I typically expect output to be much smaller in variance vs the dependent variable. This is usually the case as it is not necessary to fit extreme data values, the tree just needs to produce predictors that are large/small enough to be ranked first/last in the group.
Related
So I have been looking at XGBoost as a place to start with this, however I am not sure the best way to accomplish what I want.
My data is set up something like this
Where every value, whether it be input or output is numerical. The issue I'm facing is that I only have 3 input data points per several output data points.
I have seen that XGBoost has a multi-output regression method, however I am only really seeing it used to predict around 2 outputs per 1 input, whereas my data may have upwards of 50 output points needing to be predicted with only a handful of scalar input features.
I'd appreciate any ideas you may have.
For reference, I've been looking at mainly these two demos (they are the same idea just one is scikit and the other xgboost)
https://machinelearningmastery.com/multi-output-regression-models-with-python/
https://xgboost.readthedocs.io/en/stable/python/examples/multioutput_regression.html
I have a dataframe which contains three more or less significant correlations between target column and other columns ( LinarRegressionModel.coef_ from sklearn shows 57, 97 and 79). And I don't know what exact model to choose: should I use only most correlated column for regression or use regression with all three predictors. Is there any way to compare models effectiveness? Sorry, I'm very new to data analysis, I couldn't google any tools for this task
Well first at all, you must know that when we are choosing the best model to apply to new data, we are going to choose the best model to fit out of sample data, which is the kind of samples that might are not present in the training process, after all, you want to predict new probabilities or cases. In your case, predict a new number.
So, how can we do this? Well, the best is to use metrics which can help us to choose which model is better for our dataset.
There are so many kinds of metrics for regression:
MAE: Mean absolute error is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just the average error.
MSE: Mean squared error is the mean of the squared error. It’s more popular than a mean absolute error because the focus is geared more towards large errors.
RMSE: Root means the squared error is the square root of the mean squared error. This is one of the most popular of the evaluation metrics because root means the squared error is interpretable in the same units as the response vector or y units, making it easy to relate its information.
RAE: Relative absolute error, also known as the residual sum of a square, where y bar is a mean value of y, takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor.
You can work with any of these, but I highly recommend to use MSE and RMSE.
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 am modeling a perceptual process in tensorflow. In the setup I am interested in, the modeled agent is playing a resource game: it has to choose 1 out of n resouces, by relying only on the label that a classifier gives to the resource. Each resource is an ordered pair of two reals. The classifier only sees the first real, but payoffs depend on the second. There is a function taking first to second.
Anyway, ideally I'd like to train the classifier in the following way:
In each run, the classifier give labels to n resources.
The agent then gets the payoff of the resource corresponding to the highest label in some predetermined ranking (say, A > B > C > D), and randomly in case of draw.
The loss is taken to be the normalized absolute difference between the payoff thus obtained and the maximum payoff in the set of resources. I.e., (Payoff_max - Payoff) / Payoff_max
For this to work, one needs to run inference n times, once for each resource, before calculating the loss. Is there a way to do this in tensorflow? If I am tackling the problem in the wrong way feel free to say so, too.
I don't have much knowledge in ML aspects of this, but from programming point of view, I can see doing it in two ways. One is by copying your model n times. All the copies can share the same variables. The output of all of these copies would go into some function that determines the the highest label. As long as this function is differentiable, variables are shared, and n is not too large, it should work. You would need to feed all n inputs together. Note that, backprop will run through each copy and update your weights n times. This is generally not a problem, but if it is, I heart about some fancy tricks one can do by using partial_run.
Another way is to use tf.while_loop. It is pretty clever - it stores activations from each run of the loop and can do backprop through them. The only tricky part should be to accumulate the inference results before feeding them to your loss. Take a look at TensorArray for this. This question can be helpful: Using TensorArrays in the context of a while_loop to accumulate values
I have some comments and i want to classify them as Positive or Negative.
So far i have an annotated dataset .
The thing is that the first 100 rows are classified as positive and the rest 100 as Negative.
I am using SQL Server Analysis-2008 R2. The Class attribute has 2 values, POS-for positive and NEG-for negative.
Also i use Naive Bayes algorithm with maximum input/output attributes=0 (want to use all the attributes) for the classification, the test set max case is set to 30%. The current score from the Lift Chart is 0.60.
Do i have to mix them up, for example 2 POS followed by 1 NEG, in order to get better classification accuracy?
The ordering of the learning instances should not affect classification performance. The probabilities computed by Naive Bayes will be the same for any ordering of instances in the data set.
However, the selection of different test and training sets can affect classification performance. For example, some instances might be inherently more difficult to classify than others.
Are you getting similarly poor training and test performance? If your training performance is good and/or much better than your test performance, your model may be over-fitted. Otherwise, if your training performance is also poor, I would suggest (a) trying a better/stronger/more expressive classifier, e.g., SVM, decision trees etc; and/or (b) making sure your features are representive/expressive enough of the data.