I would like to use a supervised machine learning algorithm to predict a binary function (true or false) for a set of sentences based on the presence or absence of words in the sentences.
Ideally, I would like to avoid having to hardcode the set of words used to decide on the output so that the algorithm automatically learns which words are (together ?) most likely to trigger specific outputs.
http://shop.oreilly.com/product/9780596529321.do (Programming Collective Intelligence) has a nice section in chapter 4 titled "Learning From Clicks" which describes how to do this by using 1 layer of hiden nodes in a neural network with one new hidden node for each new combination of input words.
Similarly, it is possible to create a feature for each word in the training data set and train pretty much any classic machine learning algorithm using these features. Adding new training data will generate new features which will require me to re-train the algorithm from scratch.
Which brings me to my questions:
is it actually a problem if I have to retrain everything from scratch whenever the training data set is extended ?
what kind of algorithm would more experience machine learning users recommend to use for this kind of problem ?
what criteria should I use in picking an algorithm versus another ? (other than actually trying them all and see which perform better with precision/recall metrics)
if you have worked on similar problems, what about extending the features with 2-grams (1 if a specific 2-gram is present, 0 if not) ? 3-grams ?
You could look into the general area of topic modelling if you want to find words which are generally found together.
The most simple approach would be to use latent semantic analysis ( http://en.wikipedia.org/wiki/Latent_semantic_analysis ), which is just applying SVD to a term document matrix. You'd then need to do some additional post hoc analysis to fit this to your particular outcome.
A more involved, and much more complex approach would be to use latent dirichlet allocation ( http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation )
In terms of just adding new features (words) that is fine as long as you are going to retrain. You can also use TF/IDF to give that particular word a value when representing the matrix (Instead of just a 1 or 0).
I don't know what programming language you are trying to do this in, but I know there are libraries out there in Java and Pythont hat do all of the above.
Related
I am building a model that will predict the lead time of products flowing through a pipeline.
I have a lot of different features, one is a string containing a few words about the purpose of the product (often abbreviations, name of the application it will be a part of and so forth). I have previously not used this field at all when doing feature engineering.
I was thinking that it would be nice to do some type of clustering on this data, and then use the cluster ID as a feature for my model, perhaps the lead time is correlated with the type of info present in that field.
Here was my line of thinking)
1) Cleaning & tokenizing text.
2) TF-IDF
3) Clustering
But after thinking more about it, is it a bad idea? Because the clustering was based on the old data, if new words are introduced in the new data this will not be captured by the clustering algorithm, and the data should perhaps be clustered differently now. Does this mean that I would have to retrain the entire model (k-means model and then the supervised model) whenever I want to predict new data points? Are there any best practices for this?
Are there better ways of finding clusters for text data to use as features in a supervised model?
I understand the urge to use an unsupervised clustering algorithm first to see for yourself, which clusters were found. And of course you can try if such a way helps your task.
But as you have labeled data, you can pass the product description without an intermediate clustering. Your supervised algorithm shall then learn for itself if and how this feature helps in your task (of course preprocessing such as removal of stopwords, cleaining, tokenizing and feature extraction needs to be done).
Depending of your text descriptions, I could also imagine that some simple sequence embeddings could work as feature-extraction. An embedding is a vector of for example 300 dimensions, which describes the words in a manner that hp office printer and canon ink jet shall be close to each other but nice leatherbag shall be farer away from the other to phrases. For example fasText-Word-Embeddings are already trained in english. To get a single embedding for a sequence of hp office printerone can take the average-vector of the three vectors (there are more ways to get an embedding for a whole sequence, for example doc2vec).
But in the end you need to run tests to choose your features and methods!
I must participate in a research project regarding a deep learning application for classification. I have a huge dataset containing over 35000 features - these are good values, taken from laboratory.
The idea is that I should create a classifier that must tell, given a new input, if the data seems to be good or not. I must use deep learning with keras and tensor flow.
The problem is that the data is not classified. I will enter a new column with 1 for good and 0 for bad. Problem is, how can I find out if an entry is bad, given the fact that the whole training set is good?
I have thought about generating some garbage data but I don't know if this is a good idea - I don't even know how to generate it. Do you have any tips?
I would start with anamoly detection. You can first reduce features with f.e. an (stacked) autoencoder and then use local outlier factor from sklearn: https://scikit-learn.org/stable/modules/outlier_detection.html
The reason why you need to reduce features first is, is because your LOF will be much more stable.
I’m very new to machine learning.
I have a dataset with data given me by a f1 race. User is playing this game and is giving me this dataset.
With machine learning, I have to work with this data and when a user (I know they are 10) plays a game I have to recognize who’s playing.
The data consists of datagram packet occurred in 1/10 second freq, the packets contains the following Time, laptime, lapdistance, totaldistance, speed, car position, traction control, last lap time, fuel, gear,..
I’ve thought to use a kmeans used in a supervised way.
Which algorithm could be better?
The task must be a multiclass classification. The very first step in any machine learning activity is to define a score metric (https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/). That allows you to compare models between themselves and decide which is better. Then build a base model with random forest or/and logistic regression as suggested in another answer - they perform well out-of-the-box. Then try to play with features and understand which of them are more informative. And don't forget about a visualizations - they give many hints for data wrangling, etc.
this is somewhat a broad question, so I'll try my best
kmeans is unsupervised algorithm meaning it will find the classes itself and it best used when you know there are multiple classes but you don't know what exactly they are... using it with labeled data just means you will compute the distance of new vector v to each vector in the dataset and pick the one (or ones using majority vote) which give the min distance , this is not considered as machine learning
in this case when you do have the labels, supervised approach will yield much better results
I suggest try random forest and logistic regression at first, those are the most basic and common algorithms and they give pretty good results
if you haven't achieve the desired accuracy you can use deep learning and build a neural network with input layer as big as your packet's values and output layer of the number of classes, in between you can use one or multiple hidden layers with various nodes, but this is advanced approach and you better pick up some experience in machine learning field before pursue it
Note: the data is a time series, meaning that every driver has it's own behaviour of driving a car, so data should be considered as bulks of points, with this you can apply pattern matching technics, also there are a several neural networks build exactly for this data (like RNN) but this is far far advanced and much more difficult to implement
I have a data that represents comments from the operator on various activities performed on a industrial device. The comments, could reflect either a routine maintainence/replacement activity or could represent that some damage occured and it had to be repaired to rectify the damage.
I have a set of 200,000 sentences that needs to be classified into two buckets - Repair/Scheduled Maintainence(or undetermined). These have no labels, hence looking for an unsupervised learning based solution.
Some sample data is as shown below:
"Motor coil damaged .Replaced motor"
"Belt cracks seen. Installed new belt"
"Occasional start up issues. Replaced switches"
"Replaced belts"
"Oiling and cleaning done".
"Did a preventive maintainence schedule"
The first three sentences have to be labeled as Repair while the second three as Scheduled maintainence.
What would be a good approach to this problem. though I have some exposure to Machine learning I am new to NLP based machine learning.
I see many papers related to this https://pdfs.semanticscholar.org/a408/d3b5b37caefb93629273fa3d0c192668d63c.pdf
https://arxiv.org/abs/1611.07897
but wanted to understand if there is any standard approach to such problems
Seems like you could use some reliable keywords (verbs it seems in this case) to create training samples for an NLP Classifier. Or you could use KMeans or KMedioids clustering and use 2 as K, which would do a pretty good job of separating the set. If you want to get really involved, you could use something like Latent Derichlet Allocation, which is a form of unsupervised topic modeling. However, for a problem like this, on the small amount of data you have, the fancier you get the more frustrated with the results you will become IMO.
Both OpenNLP and StanfordNLP have text classifiers for this, so I recommend the following if you want to go the classification route:
- Use key word searches to produce a few thousand examples of your two categories
- Put those sentences in a file with a label based on the OpenNLP format (label |space| sentence | newline )
- Train a classifier with the OpenNLP DocumentClassifier, and I recommend stemming for one of your feature generators
- after you have the model, use it in Java and classify each sentence.
- Keep track of the scores, and quarantine low scores (you will have ambiguous classes I'm sure)
If you don't want to go that route, I recommend using a text indexing technology de-jeur like SOLR or ElasticSearch or your favorite RDBMS's text indexing to perform a "More like this" type function so you don't have to play the Machine learning continuous model updating game.
I've got a problem where I've potentially got a huge number of features. Essentially a mountain of data points (for discussion let's say it's in the millions of features). I don't know what data points are useful and what are irrelevant to a given outcome (I guess 1% are relevant and 99% are irrelevant).
I do have the data points and the final outcome (a binary result). I'm interested in reducing the feature set so that I can identify the most useful set of data points to collect to train future classification algorithms.
My current data set is huge, and I can't generate as many training examples with the mountain of data as I could if I were to identify the relevant features, cut down how many data points I collect, and increase the number of training examples. I expect that I would get better classifiers with more training examples given fewer feature data points (while maintaining the relevant ones).
What machine learning algorithms should I focus on to, first,
identify the features that are relevant to the outcome?
From some reading I've done it seems like SVM provides weighting per feature that I can use to identify the most highly scored features. Can anyone confirm this? Expand on the explanation? Or should I be thinking along another line?
Feature weights in a linear model (logistic regression, naive Bayes, etc) can be thought of as measures of importance, provided your features are all on the same scale.
Your model can be combined with a regularizer for learning that penalises certain kinds of feature vectors (essentially folding feature selection into the classification problem). L1 regularized logistic regression sounds like it would be perfect for what you want.
Maybe you can use PCA or Maximum entropy algorithm in order to reduce the data set...
You can go for Chi-Square tests or Entropy depending on your data type. Supervized discretization highly reduces the size of your data in a smart way (take a look into Recursive Minimal Entropy Partitioning algorithm proposed by Fayyad & Irani).
If you work in R, the SIS package has a function that will do this for you.
If you want to do things the hard way, what you want to do is feature screening, a massive preliminary dimension reduction before you do feature selection and model selection from a sane-sized set of features. Figuring out what is the sane-size can be tricky, and I don't have a magic answer for that, but you can prioritize what order you'd want to include the features by
1) for each feature, split the data in two groups by the binary response
2) find the Komogorov-Smirnov statistic comparing the two sets
The features with the highest KS statistic are most useful in modeling.
There's a paper "out there" titled "A selctive overview of feature screening for ultrahigh-dimensional data" by Liu, Zhong, and Li, I'm sure a free copy is floating around the web somewhere.
4 years later I'm now halfway through a PhD in this field and I want to add that the definition of a feature is not always simple. In the case that your features are a single column in your dataset, the answers here apply quite well.
However, take the case of an image being processed by a convolutional neural network, for example, a feature is not one pixel of the input, rather it's much more conceptual than that. Here's a nice discussion for the case of images:
https://medium.com/#ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721