I am dealing with a classification problem.
I need to model the changing trends of the features.
For example,I have some characteristic changes as follows.
Feature1(Positive)：
Feature2(Negative)：
How to model change trends of those characteristics?
Use the empirical derivative.
I.e. x.delay(some small value) - x
Related
So say for each of my ‘things’ to classify I have:
{house, flat, bungalow, electricityHeated, gasHeated, ... }
Which would be made into a feature vector:
{1,0,0,1,0,...} which would mean a house that is heated by electricity.
For my training data I would have all this data- but for the actual thing I want to classify I might only have what kind of house it is, and a couple other things- not all the data ie.
{1,0,0,?,?,...}
So how would I represent this?
I would want to find the probability that a new item would be gasHeated.
I would be using a SVM linear classifier- I don’t have any core to show because this is purely theoretical at the moment. Any help would be appreciated :)
When I read this question, it seems that you may have confused with feature and label.
You said that you want to predict whether a new item is "gasHeated", then "gasHeated" should be a label rather than a feature.
btw, one of the most-common ways to deal with missing value is to set it as "zero" (or some unused value, say -1). But normally, you should have missing value in both training data and testing data to make this trick be effective. If this only happened in your testing data but not in your training data, it means that your training data and testing data are not from the same distribution, which basically violated the basic assumption of machine learning.
Let's say you have a trained model and a testing sample {?,0,0,0}. Then you can create two new testing samples, {1,0,0,0}, {0,0,0,0}, and you will have two predictions.
I personally don't think SVM is a good approach if you have missing values in your testing dataset. Just like I have mentioned above, although you can get two new predictions, but what if each one has different predictions? It is difficult to assign a probability to results of SVM in my opinion unless you use logistic regression or Naive Bayes. I would prefer Random Forest in this situation.
I have the data as attached. I want to develop a model for predicting the oil rates in the upcoming 'x' years. Basically i want to develop a predictive model. Can someone please help me out with how should i transform this data to multiclass classification for using SVM model?
This is what my data looks like. First two attributes are Date-Time and next three are my numeric attributes out of which oil rate is the one which i want to predict in the next 'x years' by developing a predictive model.
The format of your data and the problem that you are trying to solve do not translate well into a multi class classification problem. What will your classes look like? You can force fit it to ranges of the form 100-200, 200-300 etc but that is not very informative.
A regression based approach would be better suited for this as your are interested in a real valued numerical answer.
You can start off by looking at ARIMA and other similar models that are designed for this kind of predictive task.
Is there a way to penalize a feature in so that it doesn't dominate the model? (In Salford Predictive Modeller, there is a setting called "Penalties on Variables")
The situation is that I have one categorical feature which I want to include in the model, but I don't want to have as the most important feature, since then the model doesn't properly capture the variance explained by the other predictors.
I think you cannot do that. Although I don't really understand why you would want to do that, you could try the following:
Train a model on the whole dataset, train a separate model on the dataset after removing this feature. Then, combine the results of the two models (maybe simple averaging or stacking etc.)
Suppose that for a given ML problem, we have a feature which car the person possesses. We can encode this information in one of the following ways:
Assign an id to each of the car. Make a column 'CAR_POSSESSED' and put feature id as value.
Make columns for each of the car and put 0 or 1 according to whether that car is possessed by the considered sample or not. Columns will be like "BMW_POSSESSED", "AUDI_POSSESSED".
In my experiments the 2nd way performed much better than 1st one, when tried with SVM.
How does the encoding way affects the model learning, and are there some resources in which affect of encoding has been studied? Or do we need to do hit and trials to check where it performs best?
The problem with the first way is that you use arbitrary numbers to represent the features (e.g. BMW=2, etc.) and SVM take those numbers seriously, as if they have order: e.g. it may try to use cases with CAR_OWNED>3 for the prediction.
So the second way is better.
Chapter 2.1 Categorical Features:
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
You'll find many more if you search for "svm Categorical Features"
I am doing a logistic regression to predict the outcome of a binary variable, say whether a journal paper gets accepted or not. The dependent variable or predictors are all the phrases used in these papers - (unigrams, bigrams, trigrams). One of these phrases has a skewed presence in the 'accepted' class. Including this phrase gives me a classifier with a very high accuracy (more than 90%), while removing this phrase results in accuracy dropping to about 70%.
My more general (naive) machine learning question is:
Is it advisable to remove such skewed features when doing classification?
Is there a method to check skewed presence for every feature and then decide whether to keep it in the model or not?
If I understand correctly you ask whether some feature should be removed because it is a good predictor (it makes your classifier works better). So the answer is short and simple - do not remove it in fact, the whole concept is to find exactly such features.
The only reason to remove such feature would be that this phenomena only occurs in the training set, and not in real data. But in such case you have wrong data - which does not represnt the underlying data density and you should gather better data or "clean" the current one so it has analogous characteristics as the "real ones".
Based on your comments, it sounds like the feature in your documents that's highly predictive of the class is a near-tautology: "paper accepted on" correlates with accepted papers because at least some of the papers in your database were scraped from already-accepted papers and have been annotated by the authors as such.
To me, this sounds like a useless feature for trying to predict whether a paper will be accepted, because (I'd imagine) you're trying to predict paper acceptance before the actual acceptance has been issued ! In such a case, none of the papers you'd like to test your algorithm with will be annotated with "paper accepted on." So, I'd remove it.
You also asked about how to determine whether a feature correlates strongly with one class. There are three things that come to mind for this problem.
First, you could just compute a basic frequency count for each feature in your dataset and compare those values across classes. This is probably not super informative, but it's easy.
Second, since you're using a log-linear model, you can train your model on your training dataset, and then rank each feature in your model by its weight in the logistic regression parameter vector. Features with high positive weight are indicative of one class, while features with large negative weight are strongly indicative of the other.
Finally, just for the sake of completeness, I'll point out that you might also want to look into feature selection. There are many ways of selecting relevant features for a machine learning algorithm, but I think one of the most intuitive from your perspective might be greedy feature elimination. In such an approach, you train a classifier using all N features in your model, and measure the accuracy on some held-out validation set. Then, train N new models, each with N-1 features, such that each model eliminates one of the N features, and measure the resulting drop in accuracy. The feature with the biggest drop was probably strongly predictive of the class, while features that have no measurable difference can probably be omitted from your final model. As larsmans points out correctly in the comments below, this doesn't scale well at all, but it can be a useful method sometimes.