I am classifying data with categorical variables. It is data where people have provided information.
My training dataset is of varying quality. I have a greater confidence in some of the data i.e. I have a higher confidence that people have provided correct information whereas in some the data I am not so sure.
How can I pass this information into a classification algorithm such as Naive Bayes or K nearest neighbour?
Or should I instead look to another algorithm?
I think what you want to do, is to provide individual weights (for the importance/confidence) for each data point you have.
For instance, if you are very certain that one data point is of higher quality and should have a higher weight than others, in which you are less confident in, you can specify that when fitting your classifier.
Sklearn provides for instance the Gaussian Naive Bayes classifier (GaussianNB) for that.
Here, you can specify sample_weights when calling the fit() method.
Related
I am using sklearn's random forests module to predict values based on 50 different dimensions. When I increase the number of dimensions to 150, the accuracy of the model decreases dramatically. I would expect more data to only make the model more accurate, but more features tend to make the model less accurate.
I suspect that splitting might only be done across one dimension which means that features which are actually more important get less attention when building trees. Could this be the reason?
Yes, the additional features you have added might not have good predictive power and as random forest takes random subset of features to build individual trees, the original 50 features might have got missed out. To test this hypothesis, you can plot variable importance using sklearn.
Your model is overfitting the data.
From Wikipedia:
An overfitted model is a statistical model that contains more parameters than can be justified by the data.
https://qph.fs.quoracdn.net/main-qimg-412c8556aacf7e25b86bba63e9e67ac6-c
There are plenty of illustrations of overfitting, but for instance, this 2d plot represents the different functions that would have been learned for a binary classification task. Because the function on the right has too many parameters, it learns wrongs data patterns that don't generalize properly.
If I am training a sentiment classifier off of a tagged dataset where most documents are negative, say ~95%, should the classifier be trained with the same distribution of negative comments? If not, what would be other options to "normalize" the data set?
You don't say what type of classifier you have but in general you don't have to normalize the distribution of the training set. However, usually the more data the better but you should always do blind tests to prevent over-fitting.
In your case you will have a strong classifier for negative comments and unless you have a very large sample size, a weaker positive classifier. If your sample size is large enough it won't really matter since you hit a point where you might start over-fitting your negative data anyway.
In short, it's impossible to say for sure without knowing the actual algorithm and the size of the data sets and the diversity within the dataset.
Your best bet is to carve off something like 10% of your training data (randomly) and just see how the classifier performs after being trained on the 90% subset.
I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help?
Currently there isn't a way to use class_weights for GB in sklearn.
Don't confuse this with sample_weight
Sample Weights change the loss function and your score that you're trying to optimize. This is often used in case of survey data where sampling approaches have gaps.
Class Weights are used to correct class imbalances as a proxy for over \ undersampling. There is no direct way to do that for GB in sklearn (you can do that in Random Forests though)
Very late, but I hope it can be useful for other members.
In the article of Zichen Wang in towardsdatascience.com, the point 5 Gradient Boosting it is told:
For instance, Gradient Boosting Machines (GBM) deals with class imbalance by constructing successive training sets based on incorrectly classified examples. It usually outperforms Random Forest on imbalanced dataset For instance, Gradient Boosting Machines (GBM) deals with class imbalance by constructing successive training sets based on incorrectly classified examples. It usually outperforms Random Forest on imbalanced dataset.
And a chart shows that the half of the grandient boosting model have an AUROC over 80%. So considering GB models performances and the way they are done, it seems not to be necessary to introduce a kind of class_weight parameter as it is the case for RandomForestClassifier in sklearn package.
In the book Introduction To Machine Learning with Pyhton written by Andreas C. Müller and Sarah Guido, edition 2017, page 89, Chapter 2 *Supervised Learning, section Ensembles of Decision Trees, sub-section Gradient boosted regression trees (gradient boosting machines):
They are generally a bit more sensitive to
parameter settings than random forests, but can provide better accuracy if the parameters are set correctly.
Now if you still have scoring problems due to imbalance proportions of categories in the target variable, it is possible you should see if your data should be splited to apply different models on it, because they are not as homogeneous as it seems to be. I mean it may have a variable you have not in your dataset train (an hidden variable clearly) that influences a lot the model results, then it is difficult even for the greater GB to give correct scoring because it misses a huge information that you cannot make appear in the matrix to compute sometimes for many reasons.
Some updates:
I found, by random, there are libraries that implement it as parameters of their gradient boosting instance objects. It is the case of H2O where for the parameter balance_classes it is told:
Balance training data class counts via over/under-sampling (for
imbalanced data).
Type: bool (default: False).
If you want to keep with sklearn you should do as HakunaMaData told: over/under-sampling because that's what other libraries finally do when the parameter exist.
I am working with sklearn's implementation of KNN. While my input data has about 20 features, I believe some of the features are more important than others. Is there a way to:
set the feature weights for each feature when "training" the KNN learner.
learn what the optimal weight values are with or without pre-processing the data.
On a related note, I understand generally KNN does not require training but since sklearn implements it using KDTrees, the tree must be generated from the training data. However, this sounds like its turning KNN into a binary tree problem. Is that the case?
Thanks.
kNN is simply based on a distance function. When you say "feature two is more important than others" it usually means difference in feature two is worth, say, 10x difference in other coords. Simple way to achive this is by multiplying coord #2 by its weight. So you put into the tree not the original coords but coords multiplied by their respective weights.
In case your features are combinations of the coords, you might need to apply appropriate matrix transform on your coords before applying weights, see PCA (principal component analysis). PCA is likely to help you with question 2.
The answer to question to is called "metric learning" and currently not implemented in Scikit-learn. Using the popular Mahalanobis distance amounts to rescaling the data using StandardScaler. Ideally you would want your metric to take into account the labels.
One can measure goodness of fit of a statistical model using Akaike Information Criterion (AIC), which accounts for goodness of fit and for the number of parameters that were used for model creation. AIC involves calculation of maximized value of likelihood function for that model (L).
How can one compute L, given prediction results of a classification model, represented as a confusion matrix?
It is not possible to calculate the AIC from a confusion matrix since it doesn't contain any information about the likelihood. Depending on the model you are using it may be possible to calculate the likelihood or quasi-likelihood and hence the AIC or QIC.
What is the classification problem that you are working on, and what is your model?
In a classification context often other measures are used to do GoF testing. I'd recommend reading through The Elements of Statistical Learning by Hastie, Tibshirani and Friedman to get a good overview of this kind of methodology.
Hope this helps.
Information-Based Evaluation Criterion for Classifier's Performance by Kononenko and Bratko is exactly what I was looking for:
Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier''s answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.