I'm using Weka to perform classification, clustering, and some regression on a few large data sets. I'm currently trying out all the classifiers (decision tree, SVM, naive bayes, etc.).
Is there a way (in Weka or other machine learning toolkit) to sweep through all the available classifier algorithms to find the one that produces the best cross-validated accuracy or other metric?
I'd like to find the best clustering algorithm, too, for my other clustering problem; perhaps finding the lowest sum-of-squared-error?
Isn't that some kind of overfitting, too? Trying tons of classifiers, and choosing the best?
Also note that preprocessing is usually very important, and different classifiers may need different preprocessing; and each classifier has in turn a dozen or so parameters...
Same for clustering, don't choose a clustering algorithm by some metric. Because if you choose e.g. "lowest sum-of-squares", k-means will win. Not because it is better. But because it is more overfit to your evaluation method: k-means optimizes the sum-of-squares. The results may be crap on other metrics, but on SSQ, they are by design a local optimum.
Data mining is not something you can automate to a push-button level.
It's a skill that requires experience on how to preprocess, choose algorithms, adjust parameters and evaluate the actual outcome. Otherwise, you'd have some software on the market where you just feed your data and get the optimal classifier out.
Related
I know that support vector machine, random tree forest and logistic regression are famous machine learning (ML)algorithms for classification.
I'm confused the terminology between a feature extraction, selection and classification.
Does the above ML algorithms are used for extracting features not part of selecting?
Does the ML algorithms include both process of feature extraction and classification?
Does the result of training the ML algorithm (accuracy, specificity, sensitivity..) tell us the result of classifying a disease after the feature extraction?
Regarding your confusion about the 3 terminologies,
Feature extraction: When you want to create new features out of raw data (say you have the transaction_day column but you are only interested in the month, so you create a new column "transaction_month" out of "transaction_day")
Feature selection: You have many features but want to select only the important ones (how many of them is another topic to be studied). This could speed up the process of learning and with the right strategy, you would not sacrifice accuracy in many applications.
Classification: Is a family of supervised (labeled) machine learning that your goal is to assign observations to known classes (for example emails to spam or normal class)
Note: Some of machine learning algorithms like "Lasso" have build-in feature selection but for others, large coefficient of the feature after training usually shows the importance of the feature (read more about recursive feature elimination (rfe))
you may also find a good discussion in this post.
I have a dataset that contains around 30 features and I want to find out which features contribute the most to the outcome. I have 5 algorithms:
Neural Networks
Logistics
Naive
Random Forest
Adaboost
I read a lot about Information Gain technique and it seems it is independent of the machine learning algorithm used. It is like a preprocess technique.
My question follows, is it best practice to perform feature importance for each algorithm dependently or just use Information Gain. If yes what are the technique used for each ?
First of all, it's worth stressing that you have to perform the feature selection based on the training data only, even if it is a separate algorithm. During testing, you then select the same features from the test dataset.
Some approaches that spring to mind:
Mutual information based feature selection (eg here), independent of the classifier.
Backward or forward selection (see stackexchange question), applicable to any classifier but potentially costly since you need to train/test many models.
Regularisation techniques that are part of the classifier optimisation, eg Lasso or elastic net. The latter can be better in datasets with high collinearity.
Principal components analysis or any other dimensionality reduction technique that groups your features (example).
Some models compute latent variables which you can use for interpretation instead of the original features (e.g. Partial Least Squares or Canonical Correlation Analysis).
Specific classifiers can aid interpretability by providing extra information about the features/predictors, off the top of my head:
Logistic regression: you can obtain a p-value for every feature. In your interpretation you can focus on those that are 'significant' (eg p-value <0.05). (same for two-classes Linear Discriminant Analysis)
Random Forest: can return a variable importance index that ranks the variables from most to least important.
I have a dataset that contains around 30 features and I want to find out which features contribute the most to the outcome.
This will depend on the algorithm. If you have 5 algorithms, you will likely get 5 slightly different answers, unless you perform the feature selection prior to classification (eg using mutual information). One reason is that Random Forests and neural networks would pick up nonlinear relationships while logistic regression wouldn't. Furthermore, Naive Bayes is blind to interactions.
So unless your research is explicitly about these 5 models, I would rather select one model and proceed with it.
Since your purpose is to get some intuition on what's going on, here is what you can do:
Let's start with Random Forest for simplicity, but you can do this with other algorithms too. First, you need to build a good model. Good in the sense that you need to be satisfied with its performance and it should be Robust, meaning that you should use a validation and/or a test set. These points are very important because we will analyse how the model takes its decisions, so if the model is bad you will get bad intuitions.
After having built the model, you can analyse it at two level : For the whole dataset (understanding your process), or for a given prediction. For this task I suggest you to look at the SHAP library which computes features contributions (i.e how much does a feature influences the prediction of my classifier) that can be used for both puproses.
For detailled instructions about this process and more tools, you can look fast.ai excellent courses on the machine learning serie, where lessons 2/3/4/5 are about this subject.
Hope it helps!
What are some real world applications where incremental learning of (machine learning) algorithms is useful?
Are SVMs preferred for such applications?
Is the solution more computationally intensive than retraining with the set containing old support vectors and new training vectors ?
There is a well known incremental version of SVM:
http://www.isn.ucsd.edu/pubs/nips00_inc.pdf
However, there are not much existing implementations available, maybe something is in Matlab:
http://www.isn.ucsd.edu/svm/incremental/
The advantage of that approach is that it offers exact leave-one-out evaluation of
the generalization performance on the training data
theres is a trend towards large, "out of core" datasets, which are often streamed in from network, disk, or a database. a real world example is the popular nyc taxi dataset, which, at 330+gb, cannot be easily tackled by desktop statistical models.
svms, as a "one batch" algorithm, must load the entire dataset into memory. as such they are not preferred for incremental learning. rather, learners like logistic regression, kmeans, neural nets, which are capable of partial learning, are preferred for such tasks.
I am intended to do a yes/no classifier. The problem is that the data does not come from me, so I have to work with what I have been given. I have around 150 samples, each sample contains 3 features, these features are continuous numeric variables. I know the dataset is quite small. I would like to make you two questions:
A) What would be the best machine learning algorithm for this? SVM? a neural network? All that I have read seems to require a big dataset.
B)I could make the dataset a little bit bigger by adding some samples that do not contain all the features, only one or two. I have read that you can use sparse vectors in this case, is this possible with every machine learning algorithm? (I have seen them in SVM)
Thanks a lot for your help!!!
My recommendation is to use a simple and straightforward algorithm, like decision tree or logistic regression, although, the ones you refer to should work equally well.
The dataset size shouldn't be a problem, given that you have far more samples than variables. But having more data always helps.
Naive Bayes is a good choice for a situation when there are few training examples. When compared to logistic regression, it was shown by Ng and Jordan that Naive Bayes converges towards its optimum performance faster with fewer training examples. (See section 4 of this book chapter.) Informally speaking, Naive Bayes models a joint probability distribution that performs better in this situation.
Do not use a decision tree in this situation. Decision trees have a tendency to overfit, a problem that is exacerbated when you have little training data.
I'm using an OpenCV Haar classifier in my work but I keep reading conflicting reports on whether the OpenCV Haar classifier is an SVM or not, can anyone clarify if it is using an SVM? Also if it is not using an SVM what advantages does the Haar method offer over an SVM approach?
SVM and Boosting (AdaBoost, GentleBoost, etc) are feature classification strategies/algorithms. Support Vector Machines solve a complex optimization problem, often using kernel functions which allows us to separate samples by working in a much higher dimension feature space. On the other hand, boosting is a strategy based on combining lots of "cheap" classifiers in a smart way, which leads to a very fast classification. Those weak classifiers can be even SVM.
Haar-like features are a kind of features based in integral images and very suitable for Computer Vision problems.
This is, you can combine Haar features with any of the two classification schemes.
It isn't SVM. Here is the documentation:
http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#haar-feature-based-cascade-classifier-for-object-detection
It uses boosting (supporting AdaBoost and a variety of other similar methods -- all based on boosting).
The important difference is related to speed of evaluation is important in cascade classifiers and their stage based boosting algorithms allow very fast evaluation and high accuracy (in particular support training with many negatives), at a better balance point than an SVM for this particular application.