I'm trying to find the right ML algorithm. Let's say I have three data columns. I have a binary outcome for each column (either the data column belongs to (Group A) classification or it does not), BUT in each set of three data columns that I feed in, exactly ONE and only one column belongs to Group A.
Which algorithm can I choose to select the ONE BEST result of the three each time? Can I do this with a decision tree?
Decision tree aka ID3, can be suitable for this simple problem... best way is to check it on the data and see it's output prediction
ID3 have a problem of over fitting though
basically every classifier can do a good job on this task, if it linearly separable even SVM can be a good choice, also I'm suggesting trying basic neural network with 1/2 nodes at the output layer for classification of 2 groups
all of them are implemented via various packages and are fairly easy to use (almost any coding language)
Related
How does Decision tree predict the out come on a new Data set. Lets say with the hyper parameters I allowed my decision tree to grow only to a certain extent to avoid over fitting. Now a new data point is passed to this trained model, so the new data point reaches to one of the leaf nodes. But how does that leaf node predict whether the data point is either 1 or 0? ( I am talking about Classification here).
Well, you pretty much answered your own question. But just to the extension, in the last the data is labelled to either 0 or 1 is hgihly dependent on the type of algorithm you used, for example, ID3 , uses the mode value to predict. similarly C4.5 and C5 or CART have there different criteria based on info gain, ginni index etc etc....
In simplified terms, the process of training a decision tree and predicting the target features of query instances is as follows:
Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature
Train the decision tree model by continuously splitting the target feature along the values of the descriptive features using a measure of information gain during the training process
Grow the tree until we accomplish a stopping criteria --> create leaf nodes which represent the predictions we want to make for new query instances
Show query instances to the tree and run down the tree until we arrive at leaf nodes
DONE - Congratulations you have found the answers to your questions
here is a link I am suggesting which explain the decision tree in very detail with scratch. Give it a good read -
https://www.python-course.eu/Decision_Trees.php
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 am working to setup data for an unsupervised learning algorithm. The goal of the project is to group (cluster) different customers together based on their behavior on the website. Obviously, some sort of clustering algorithm is best for discovering patterns in the data we can't see as humans.
However, the database contains multiple rows for each customer (in chronological order) for each action the customer took on the website for that visit. For example customer with ID# 123 clicked on page 1 at time X and that would be a row in the database, and then the same customer clicked another page at time Y. That would make another row in the database.
My question is what algorithm or approach would you use for clustering in this given scenario? K-means is really popular for this type of problem, but I don't know if it's possible to use in this situation because of the grouping. Is it somehow possible to do cluster analysis around one specific ID that includes multiple rows?
Any help/direction of unsupervised learning I should take is appreciated.
In short,
Learn a fixed-length embedding (representation) of each event;
Learn a way to combine a sequence of such embeddings into a single representation for each event, then use your favorite unsupervised methods.
For (1), you can do it either manually or use an encoder/decoder;
For (2), there is a range of things you can do, ranging from just simply averaging embeddings from each event, to training an encoder-decoder on reconstructing the original sequence of events and take the intermediate representation (that the decoder uses to reconstruct the original sequence).
A good read on this topic (though a bit old; you now also have the option of Transformer Network):
Representations for Language: From Word Embeddings to Sentence Meanings
I'm trying to classify some data using knime with knime-labs deep learning plugin.
I have about 16.000 products in my DB, but I have about 700 of then that I know its category.
I'm trying to classify as much as possible using some DM (data mining) technique. I've downloaded some plugins to knime, now I have some deep learning tools as some text tools.
Here is my workflow, I'll use it to explain what I'm doing:
I'm transforming the product name into vector, than applying into it.
After I train a DL4J learner with DeepMLP. (I'm not really understand it all, it was the one that I thought I got the best results). Than I try to apply the model in the same data set.
I thought I would get the result with the predicted classes. But I'm getting a column with output_activations that looks that gets a pair of doubles. when sorting this column I get some related date close to each other. But I was expecting to get the classes.
Here is a print of the result table, here you can see the output with the input.
In columns selection it's getting just the converted_document and selected des_categoria as Label Column (learning node config). And in Predictor node I checked the "Append SoftMax Predicted Label?"
The nom_produto is the text column that I'm trying to use to predict the des_categoria column that it the product category.
I'm really newbie about DM and DL. If you could get me some help to solve what I'm trying to do would be awesome. Also be free to suggest some learning material about what attempting to achieve
PS: I also tried to apply it into the unclassified data (17,000 products), but I got the same result.
I won't answer with a workflow on this one because it is not going to be a simple one. However, be sure to find the text mining example on the KNIME server, i.e. the one that makes use of the bag of words approach.
The task
Product mapping to categories should be a straight-forward data mining task because the information that explains the target variable is available in a quasi-exhaustive manner. Depending on the number of categories to train though, there is a risk that you might need more than 700 instances to learn from.
Some resources
Here are some resources, only the first one being truly specialised in text mining:
Introduction on Information Retrieval, in particular chapter 13;
Data Science for Business is an excellent introduction to data mining, including text mining (chapter 10), also do not forget the chapter about similarity (chapter 6);
Machine Learning with R has the advantage of being accessible enough (chapter 4 provides an example of text classification with R code).
Preprocessing
First, you will have to preprocess your product labels a bit. Use KNIME's text analytics preprocessing nodes for that purpose, that is after you've transformed the product labels with Strings to Document:
Case Convert, Punctuation Erasure and Snowball Stemmer;
you probably won't need Stop Word Filter, however, there may be quasi-stop words such as "product", which you may need to remove manually with Dictionary Filter;
Be careful not to use any of the following without testing testing their impact first: N Chars Filter (g may be a useful word), Number Filter (numbers may indicate quantities, which may be useful for classification).
Should you encounter any trouble with the relevant nodes (e.g. Punctuation Erasure can be tricky amazingly thanks to the tokenizer), you can always apply String Manipulation with regex before converting the Strings to Document.
Keep it short and simple: the lookup table
You could build a lookup table based on the 700 training instances. The book Data mining techniques as well as resource (2) present this approach in some detail. If any model performs any worse than the lookup table, you should abandon the model.
Nearest neighbors
Neural networks are probably overkill for this task.
Start with a K Nearest Neighbor node (applying a string distance such as Cosine, Levensthein or Jaro-Winkler). This approach requires the least amount of data wrangling. At the very least, it will provide an excellent baseline model, so it is most definitely worth a shot.
You'll need to tune the parameter k and to experiment with the distance types. The Parameter Optimization Loop pair will help you with optimizing k, you can include a Cross-Validation meta node inside of the said loop to obtain an estimate of the expected performance given k instead of only one point estimate per value of k. Use Cohen's Kappa as an optimization criterion, as proposed by the resource number (3) and available via the Scorer node.
After the parameter tuning, you'll have to evaluate the relevance of your model using yet another Cross-Validation meta node, then follow up with a Loop pair including Scorer to calculate the descriptives on performance metric(s) per iteration, finally use Statistics. Kappa is a convenient metric for this task because the target variable consists of many product categories.
Don't forget to test its performance against the lookup table.
What next ?
Should lookup table or k-nn work well for you, then there's nothing else to add.
Should any of those approaches fail, you might want to analyse the precise cases on which it fails. In addition, training set size may be too low, so you could manually classify another few hundred or thousand instances.
If after increasing the training set size, you are still dealing with a bad model, you can try the bag of words approach together with a Naive Bayes classifier (see chapter 13 of the Information Retrieval reference). There is no room here to elaborate on the bag of words approach and Naive Bayes but you'll find the resources here above useful for that purpose.
One last note. Personally, I find KNIME's Naive Bayes node to perform poorly, probably because it does not implement Laplace smoothening. However, KNIME's R Learner and R Predictor nodes will allow you to use R's e1071 package, as demonstrated by resource (3).
I have a decision tree that is trained on the columns (Age, Sex, Time, Day, Views,Clicks) which gets classified into two classes - Yes or No - which represents buying decision for an item X.
Using these values,
I'm trying to predict the probability of 1000 samples(customers) which look like ('12','Male','9:30','Monday','10','3'),
('50','Female','10:40','Sunday','50','6')
........
I want to get the individual probability or a score which will help me recognize which customers are most likely to buy the item X. So i want to be able to sort the predictions and show a particular item to only 5 customers who will want to buy the item X.
How can I achieve this ?
Will a decision tree serve the purpose?
Is there any other method?
I'm new to ML so please forgive me for any vocabulary errors.
Using decision tree with a small sample set, you will definitely run into overfitting problem. Specially at the lower levels of the decision, where tree you will have exponentially less data to train your decision boundaries. Your data set should have a lot more samples than the number of categories, and enough samples for each categories.
Speaking of decision boundaries, make sure you understand how you are handling data type for each dimension. For example, 'sex' is a categorical data, where 'age', 'time of day', etc. are real valued inputs (discrete/continuous). So, different part of your tree will need different formulation. Otherwise, your model might end up handling 9:30, 9:31, 9:32... as separate classes.
Try some other algorithms, starting with simple ones like k-nearest neighbour (KNN). Have a validation set to test each algorithm. Use Matlab (or similar software) where you can use libraries to quickly try different methods and see which one works best. There is not enough information here to recommend you something very specific. Plus,
I suggest you try KNN too. KNN is able to capture affinity in data. Say, a product X is bought by people around age 20, during evenings, after about 5 clicks on the product page. KNN will be able to tell you how close each new customer is to the customers who bought the item. Based on this you can just pick the top 5. Very easy to implement and works great as a benchmark for more complex methods.
(Assuming views and clicks means the number of clicks and views by each customer for product X)
A decision tree is a classifier, and in general it is not suitable as a basis for a recommender system. But, given that you are only predicting the likelihood of buying one item, not tens of thousands, it kind of makes sense to use a classifier.
You simply score all of your customers and retain the 5 whose probability of buying X is highest, yes. Is there any more to the question?