I have a set of labeled training data, and I am training a ML algorithm to predict the label. However, some of my data points are more important than others. Or, analogously, these points have less uncertainty than the others.
Is there a general method to include an importance-representing weight to each training point in the model? Are there instead some specific models which are capable of this while others are not?
I can imagine duplicating these points (and perhaps smearing their features slightly to avoid exact duplicates), or downsampling the less important points. Is there a more elegant way to approach this problem?
Scikit-learn allows you to pass an array of sample weights while fitting the model. Vowpal Wabbit (an online ML library) also has this option.
Related
I am working to create an MLP model on a CEA Classification Dataset (Binary Classification). Each sample contains different 4 features, such as resistance and other values, each in its own range (resistance in hundreds, another in micros, etc.). I am still new to machine learning and this is the first real model to build. How can I deal with such data? I have tried feeding each sample to the neural network with a sigmoid activation function, but I am not getting accurate results. My assumption to deal with this kind of data is to scale it? If so, what are some resources which are useful to look at, since I do not quite understand when is scaling required.
Scaling your data can be an important step in building a machine-learning model, especially when working with neural networks. Scaling can help to ensure that all of the features in your dataset are on a similar scale, which can make it easier for the model to learn.
There are a few different ways to scale your data, such as normalization and standardization. Normalization is the process of scaling the data so that it has a minimum value of 0 and a maximum value of 1. Standardization is the process of scaling the data so that it has a mean of 0 and a standard deviation of 1.
When working with your CEA Classification dataset, it might be helpful to try both normalization and standardization to see which one works better for your specific dataset. You can use scikit-learn library's preprocessing functions like MinMaxScaler() and StandardScaler() for normalization and standardization respectively.
Additionally, it might be helpful to try different activation functions, such as ReLU or LeakyReLU, to see if they lead to more accurate results. Also, you can try adding more layers and neurons in your neural network to see if it improves the performance.
It's also important to remember that feature engineering, which includes the process of selecting the most important features, can be more important than scaling.
I have a dataset let say data1,data2,data3... output or predictive data should be names of people based on the given dataset. I have a training dataset but not sure which ML algorithm to use. And the list of peoples name does not change.
It sounds like you are doing a classification task, so preferably you should use a classification algorithm. The type of algorithm to use really depends on the quality and structure of your data and its decision boundaries. Typically, before one embarks on a classification task, you must identify your data's outliers, noise, class imbalances, missing values and other data quality issues. And from there, you should select a model that best suits your needs.
For example, if your model contains lots of outliers and missing values, a decision tree might be preferable. However, if you have a large class imbalance, anomaly detection may be better suited. If you decision boundary is linear, you could make use of support vector machines. While if you have non-linear decision boundaries you'll need to look into more complex models such as gaussian discriminative models, self-organizing maps, or neural networks.
In summary, it is entirely dependent on your data.
I've got a set of F features e.g. Lab color space, entropy. By concatenating all features together, I obtain a feature vector of dimension d (between 12 and 50, depending on which features selected.
I usually get between 1000 and 5000 new samples, denoted x. A Gaussian Mixture Model is then trained with the vectors, but I don't know which class the features are from. What I know though, is that there are only 2 classes. Based on the GMM prediction I get a probability of that feature vector belonging to class 1 or 2.
My question now is: How do I obtain the best subset of features, for instance only entropy and normalized rgb, that will give me the best classification accuracy? I guess this is achieved, if the class separability is increased, due to the feature subset selection.
Maybe I can utilize Fisher's linear discriminant analysis? Since I already have the mean and covariance matrices obtained from the GMM. But wouldn't I have to calculate the score for each combination of features then?
Would be nice to get some help if this is a unrewarding approach and I'm on the wrong track and/or any other suggestions?
One way of finding "informative" features is to use the features that will maximise the log likelihood. You could do this with cross validation.
https://www.cs.cmu.edu/~kdeng/thesis/feature.pdf
Another idea might be to use another unsupervised algorithm that automatically selects features such as an clustering forest
http://research.microsoft.com/pubs/155552/decisionForests_MSR_TR_2011_114.pdf
In that case the clustering algorithm will automatically split the data based on information gain.
Fisher LDA will not select features but project your original data into a lower dimensional subspace. If you are looking into the subspace method
another interesting approach might be spectral clustering, which also happens
in a subspace or unsupervised neural networks such as auto encoder.
I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied.
Problem Statement:
I have large number(say 3000)key words.These need to be classified into seven fixed categories.Each category is having training data(sample keywords).I need to come with a algorithm, when a new keyword is passed to that,it should predict to which category this key word belongs to.
I am not aware of which text classification technique need to applied for this.do we have any tools that can be used.
Please help.
Thanks in advance.
This comes under linear classification. You can use naive-bayes classifier for this. Most of the ml frameworks will have an implementation for naive-bayes. ex: mahout
Yes, I would also suggest to use Naive Bayes, which is more or less the baseline classification algorithm here. On the other hand, there are obviously many other algorithms. Random forests and Support Vector Machines come to mind. See http://machinelearningmastery.com/use-random-forest-testing-179-classifiers-121-datasets/ If you use a standard toolkit, such as Weka, Rapidminer, etc. these algorithms should be available. There is also OpenNLP for Java, which comes with a maximum entropy classifier.
You could use the Word2Vec Word Cosine distance between descriptions of each your category and keywords in the dataset and then simple match each keyword to a category with the closest distance
Alternatively, you could create a training dataset from already matched to category, keywords and use any ML classifier, for example, based on artificial neural networks by using vectors of keywords Cosine distances to each category as an input to your model. But it could require a big quantity of data for training to reach good accuracy. For example, the MNIST dataset contains 70000 of the samples and it allowed me reach 99,62% model's cross validation accuracy with a simple CNN, for another dataset with only 2000 samples I was able reached only about 90% accuracy
There are many classification algorithms. Your example looks to be a text classification problems - some good classifiers to try out would be SVM and naive bayes. For SVM, liblinear and libshorttext classifiers are good options (and have been used in many industrial applcitions):
liblinear: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
libshorttext:https://www.csie.ntu.edu.tw/~cjlin/libshorttext/
They are also included with ML tools such as scikit-learna and WEKA.
With classifiers, it is still some operation to build and validate a pratically useful classifier. One of the challenges is to mix
discrete (boolean and enumerable)
and continuous ('numbers')
predictive variables seamlessly. Some algorithmic preprocessing is generally necessary.
Neural networks do offer the possibility of using both types of variables. However, they require skilled data scientists to yield good results. A straight-forward option is to use an online classifier web service like Insight Classifiers to build and validate a classifier in one go. N-fold cross validation is being used there.
You can represent the presence or absence of each word in a separate column. The outcome variable is desired category.
When given the dataset, normally m instances by n features matrix, how to choose the classifier that is most appropriate for the dataset.
This is just like what algorithm to solve a prime Number. Not every algorithm solve any problem means each problem assigned which finite no. of algorithm. In machine learning you can apply different algorithm on a type of problem.
If matrix contain real numbered features then you can use KNN algorithm can be used. Or if matrix have words as feature then you can use naive bayes classifier which is one of best for text classification. And Machine learning have tons of algorithm you can read them apply to your problem which fits best. Hope you understand what I said.
An interesting but much more general map I found:
http://scikit-learn.org/stable/tutorial/machine_learning_map/
If you have weka, you can use experimenter and choose different algorithms on same data set to evaluate different models.
This project compares many different classifiers on different typical datasets.
If you have no idea, you could use this simple tool auto-weka which will test all the different classifiers you selected within different constraints. Before using auto-weka, you may need to convert your data to ARFF using Weka or just manually (many tutorial on youtube).
The best classifier depends on your data (binary/string/real/tags, patterns, distribution...), what kind of output to predict (binary class / multi-class / evolving classes / a value from regression ?) and the expected performance (time, memory, accuracy). It would also depend on whether you want to update your model frequently or not (ie. if it is a stream, better use an online classifier).
Please note that the best classifier may not be one but an ensemble of different classifiers.