I have a data file which has features of different mobile devices. One column with categorical data type has 1421 distinct types of values. I am trying to train a logistic regression model along with other data that I have.
My question is: Will the high cardinality column described above affect the model I am training? If yes, how do I go about preprocessing this column so that it has lower number of distinct values?
You can calculate weight of evidence(WOE) to transform your numeric or categorical variable. Refer to this link http://www.kdnuggets.com/2016/08/include-high-cardinality-attributes-predictive-model.html for understanding WOE.
The best you could do here is that to group the features using what domain knowledge you have. For example phones by brand. If you do not have that information what you could do is that you could group the features by frequency. For example any feature that is not represented more than 5% of the data, you could group as others. You could use both of these methods together as well. For more information please refer this article.
Since logistic regression is distance based model (mostly least squares method) it suffers from the curse of dimensionality.
Hope this helps though quite late.
thanks
Michael
Typically, dimensionality reduction tasks (such as PCA and FA) are performed in order to decide which features are the most significant.
For example, in the case of PCA which is the most popular and easily employed dimensionality reduction task, significance is defined by largest variation of values.
By performing PCA, you "wash" out variables that are insignificant yet can cause overfitting. I suggestyou familiarize yourself with topics such as PCA, FA and SVD.
Related
Let's assume you have 1,400 columns/data points for 200k entries and your goal is to determine which of these columns show the most signal towards a simple classification task.
I've already removed columns with a threshold of null values, low variance, bad and also too many levels for categorical, and I still have 900+ columns.
I can use lasso if I only include the 500+ numerical columns, but if I try to include the categorical as well I keep crashing, it's too much data to process.
How would you go about further reducing features in that case? My goal, more than the classification itself, is to identify the features that bring in the most information towards the classification task.
You could use a data driven approach, for example the most simple one would be to use the L1 regularisation on a logistic regression (with your simple classification task) and looking at the weights you select the ones that are not zero or close to zero.
Basically the L1 norm on the model weights enforces the sparsity of the weights vector, and in doing so, the only surviving weight are the ones corresponding to the "important" features.
In any case be careful and normalise the data before using this technique and also be careful about categorical and scalar features...
You can also use a Neural network, and then compute the gradient w.r.t. the input to see what influences the decision more.
Or some other technique like: https://link.springer.com/chapter/10.1007/978-3-030-33778-0_24
Alternatively you can also use a Random Forest model and do feature importance like: https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html
I'm wondering if any of the estimators that Sci-kit Learn provides is affected by the order of the columns in the dataframe by which it is being trained. I tried establishing a baseline by using ExtraTreesRegressor and it came out to 3 different scores:
.531687 for the regular order
.535309 for the reverse order
.554458 for the regular order
Obviously ExtraTreesRegressor is not a good example here, so I tried LinearRegression but it gave .295898 no matter what the order of the columns were.
What I want to know is if there are ANY estimators that are affected by the order of the columns and if there are not then can you point me in the direction of some way, or provide some code, that I can use to make sure that the order of the columns does matter?
Any algorithm that involves some randomness in selecting features while building the model is expected to be affected from their order; AFAIK, the only cases present in scikit-learn are the Extra Trees and the Random Forest (in both their incarnations as classifiers or regressors), which indeed share some similarities.
The smoking gun for such a behavior is the argument max_features; from the RF docs (the description is identical in the Extra Trees as well):
max_features : {“auto”, “sqrt”, “log2”} int or float, default=”auto”
The number of features to consider when looking for the best split
I am not aware of other algorithms that involve such kind of random feature selection (linear models, decision trees, SVMs, naive Bayes, neural nets, and gradient boosted trees do not), but if you glimpse something similar enough in the documentation, you can bet that the respective algorithm is also affected by the order of the features.
Keep in mind that such slight discrepancies that should not happen in theory are rather to be expected in models where randomness enters from way too many angles. For a similar case with RF in R (slightly different results when asking for importance=TRUE), check my answer in Why does the importance parameter influence performance of Random Forest in R?
I'm working on a regression algorithm, in this case k-NearestNeighbors to predict a certain price of a product.
So I have a Training set which has only one categorical feature with 4 possible values. I've dealt with it using a one-to-k categorical encoding scheme which means now I have 3 more columns in my Pandas DataFrame with a 0/1 depending the value present.
The other features in the DataFrame are mostly distances like latitud - longitude for locations and prices, all numerical.
Should I standardize (Gaussian distribution with zero mean and unit variance) and normalize before or after the categorical encoding?
I'm thinking it might be benefitial to normalize after encoding so that every feature is to the estimator as important as every other when measuring distances between neighbors but I'm not really sure.
Seems like an open problem, thus I'd like to answer even though it's late. I am also unsure how much the similarity between the vectors would be affected, but in my practical experience you should first encode your features and then scale them. I have tried the opposite with scikit learn preprocessing.StandardScaler() and it doesn't work if your feature vectors do not have the same length: scaler.fit(X_train) yields ValueError: setting an array element with a sequence. I can see from your description that your data have a fixed number of features, but I think for generalization purposes (maybe you have new features in the future?), it's good to assume that each data instance has a unique feature vector length. For instance, I transform my text documents into word indices with Keras text_to_word_sequence (this gives me the different vector length), then I convert them to one-hot vectors and then I standardize them. I have actually not seen a big improvement with the standardization. I think you should also reconsider which of your features to standardize, as dummies might not need to be standardized. Here it doesn't seem like categorical attributes need any standardization or normalization. K-nearest neighbors is distance-based, thus it can be affected by these preprocessing techniques. I would suggest trying either standardization or normalization and check how different models react with your dataset and task.
After. Just imagine that you have not numerical variables in your column but strings. You can't standardize strings - right? :)
But given what you wrote about categories. If they are represented with values, I suppose there is some kind of ranking inside. Probably, you can use raw column rather than one-hot-encoded. Just thoughts.
You generally want to standardize all your features so it would be done after the encoding (that is assuming that you want to standardize to begin with, considering that there are some machine learning algorithms that do not need features to be standardized to work well).
So there is 50/50 voting on whether to standardize data or not.
I would suggest, given the positive effects in terms of improvement gains no matter how small and no adverse effects, one should do standardization before splitting and training estimator
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
I am using a Naive Bayes Classifier to categorize several thousand documents into 30 different categories. I have implemented a Naive Bayes Classifier, and with some feature selection (mostly filtering useless words), I've gotten about a 30% test accuracy, with 45% training accuracy. This is significantly better than random, but I want it to be better.
I've tried implementing AdaBoost with NB, but it does not appear to give appreciably better results (the literature seems split on this, some papers say AdaBoost with NB doesn't give better results, others do). Do you know of any other extensions to NB that may possibly give better accuracy?
In my experience, properly trained Naive Bayes classifiers are usually astonishingly accurate (and very fast to train--noticeably faster than any classifier-builder i have everused).
so when you want to improve classifier prediction, you can look in several places:
tune your classifier (adjusting the classifier's tunable paramaters);
apply some sort of classifier combination technique (eg,
ensembling, boosting, bagging); or you can
look at the data fed to the classifier--either add more data,
improve your basic parsing, or refine the features you select from
the data.
w/r/t naive Bayesian classifiers, parameter tuning is limited; i recommend to focus on your data--ie, the quality of your pre-processing and the feature selection.
I. Data Parsing (pre-processing)
i assume your raw data is something like a string of raw text for each data point, which by a series of processing steps you transform each string into a structured vector (1D array) for each data point such that each offset corresponds to one feature (usually a word) and the value in that offset corresponds to frequency.
stemming: either manually or by using a stemming library? the popular open-source ones are Porter, Lancaster, and Snowball. So for
instance, if you have the terms programmer, program, progamming,
programmed in a given data point, a stemmer will reduce them to a
single stem (probably program) so your term vector for that data
point will have a value of 4 for the feature program, which is
probably what you want.
synonym finding: same idea as stemming--fold related words into a single word; so a synonym finder can identify developer, programmer,
coder, and software engineer and roll them into a single term
neutral words: words with similar frequencies across classes make poor features
II. Feature Selection
consider a prototypical use case for NBCs: filtering spam; you can quickly see how it fails and just as quickly you can see how to improve it. For instance, above-average spam filters have nuanced features like: frequency of words in all caps, frequency of words in title, and the occurrence of exclamation point in the title. In addition, the best features are often not single words but e.g., pairs of words, or larger word groups.
III. Specific Classifier Optimizations
Instead of 30 classes use a 'one-against-many' scheme--in other words, you begin with a two-class classifier (Class A and 'all else') then the results in the 'all else' class are returned to the algorithm for classification into Class B and 'all else', etc.
The Fisher Method (probably the most common way to optimize a Naive Bayes classifier.) To me,
i think of Fisher as normalizing (more correctly, standardizing) the input probabilities An NBC uses the feature probabilities to construct a 'whole-document' probability. The Fisher Method calculates the probability of a category for each feature of the document then combines these feature probabilities and compares that combined probability with the probability of a random set of features.
I would suggest using a SGDClassifier as in this and tune it in terms of regularization strength.
Also try to tune the formula in TFIDF you're using by tuning the parameters of TFIFVectorizer.
I usually see that for text classification problems SVM or Logistic Regressioin when trained one-versus-all outperforms NB. As you can see in this nice article by Stanford people for longer documents SVM outperforms NB. The code for the paper which uses a combination of SVM and NB (NBSVM) is here.
Second, tune your TFIDF formula (e.g. sublinear tf, smooth_idf).
Normalize your samples with l2 or l1 normalization (default in Tfidfvectorization) because it compensates for different document lengths.
Multilayer Perceptron, usually gets better results than NB or SVM because of the non-linearity introduced which is inherent to many text classification problems. I have implemented a highly parallel one using Theano/Lasagne which is easy to use and downloadable here.
Try to tune your l1/l2/elasticnet regularization. It makes a huge difference in SGDClassifier/SVM/Logistic Regression.
Try to use n-grams which is configurable in tfidfvectorizer.
If your documents have structure (e.g. have titles) consider using different features for different parts. For example add title_word1 to your document if word1 happens in the title of the document.
Consider using the length of the document as a feature (e.g. number of words or characters).
Consider using meta information about the document (e.g. time of creation, author name, url of the document, etc.).
Recently Facebook published their FastText classification code which performs very well across many tasks, be sure to try it.
Using Laplacian Correction along with AdaBoost.
In AdaBoost, first a weight is assigned to each data tuple in the training dataset. The intial weights are set using the init_weights method, which initializes each weight to be 1/d, where d is the size of the training data set.
Then, a generate_classifiers method is called, which runs k times, creating k instances of the Naïve Bayes classifier. These classifiers are then weighted, and the test data is run on each classifier. The sum of the weighted "votes" of the classifiers constitutes the final classification.
Improves Naive Bayes classifier for general cases
Take the logarithm of your probabilities as input features
We change the probability space to log probability space since we calculate the probability by multiplying probabilities and the result will be very small. when we change to log probability features, we can tackle the under-runs problem.
Remove correlated features.
Naive Byes works based on the assumption of independence when we have a correlation between features which means one feature depends on others then our assumption will fail.
More about correlation can be found here
Work with enough data not the huge data
naive Bayes require less data than logistic regression since it only needs data to understand the probabilistic relationship of each attribute in isolation with the output variable, not the interactions.
Check zero frequency error
If the test data set has zero frequency issue, apply smoothing techniques “Laplace Correction” to predict the class of test data set.
More than this is well described in the following posts
Please refer below posts.
machinelearningmastery site post
Analyticvidhya site post
keeping the n size small also make NB to give high accuracy result. and at the core, as the n size increase its accuracy degrade,
Select features which have less correlation between them. And try using different combination of features at a time.