# Tag Info

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If all your data frame are the same ( colnames and types) you can use list comprehension to change format of your data frame. Here is an example solution with two data frames: raw_data = { 'subject_id': ['1', '2', '3', '4', '5'], 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', '...

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Skewness and Kurtosis are similar, but different. Skewness is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in data distribution. It differentiates extreme values in one versus the other tail. A symmetrical distribution will have a skewness of 0. Kurtosis is all about the tails of ...

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Start with the most frequent used ones... then explore the need to build a more complex model. Chances are the distribution of service is skewed and you don’t really need to build a model outside a top n.

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There are a few different perspectives from which to determine the amount of data you need. Those include: Project complexity: Each parameter that your model has to consider in order to perform its task increases the amount of data that it will need for training. Training method: As your models is forced to understand a greater number of interlinking ...

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There are a number of existing ASR datasets on the web, though I couldn't find one specifically for Somali. If you do happen to find one, and it doesn't cover the words interested in, you can extend it with the g2p tool. If you want to make a pronunciation dictionary from scratch, phonetic dictionaries are usually bootstrapped with handwritten rules. You ...

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Welcome to the community! Very interesting question! I start with an introduction and the propose a few solutions: If you did not have labels (seems like you have as you colored them) then there was not any justifiable argument for that. Evaluation of unsupervised tasks are theoretically impossible as you already mentioned (what is close? or what is clear?)...

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Since the variables isPWKinText and H1_2_Len have the same value for all examples in your dataset they have zero variance and contain no information. There is simply just no inference you can make based on them. That is why they are not relevant and shown blank. Please note, that this depends on your dataset, of course. The two variable might just be ...

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The ml model with single user data would be highly biased and if you try that model ( using features of only one person) on another person, will have poor accuracy. The more data you have, the less bias it has, it will fit better for predicting future values.

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The model would perform better when given similar data as its trained on.i think you should try mixing the two datasets and check the results. To the client, you can show the three results and explain how it is a great product. In place of mAp, show a chart of precision and recall to explain the accuracy.

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In ML, all algorithms are useful depending on the dataset. Its naive to generalize an algorithm to be always better than the other. In your case since you have only 90 examples, mlpclassifier couldn’t have trained proper as compared to logreg. A general suggestion: if you post like rows of your dataset along with accuracy of the two algorithms, it would ...

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You could: Label manually few of them (say 100-150), then train a simple model to classify data. A small Random Forest could do the job well. Train a super basic model on the each dataset used to produce each scatterplot. Something like a Linear Classifier. If the Classifier doesn't make mistakes, you have "clearly separated data", if it makes mistakes then ...

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You need normal data to train on. If you have abnormal instances also, those should be excluded from the training set. Having access to labeled abnormal/normal data is very useful for the validation and testset. Anything that differs from the normal data (as learned by the autoencoder) is considered an anomaly. If you have a lot of labeled abnormal and ...

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I had posted a similar question in reddit as well and I got a response from Nikolay Shmyrev : "If you want to convert latin script, you can write simple rules yourself. Something like this. Or you can use epitran as is. " Thanks to Nikolay Shmyrev who originally answered in reddit.

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In general tidy data is great... but it can quickly become unreasonably large. This is the main reason why I usually try to refactor my data in a tidy format as late as possible in the process. Example: imagine a dataset containing $N$ instances, with columns feature1 ... featureX and result1... resultY, where the result? columns represent some value based ...

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Well, what you should do depends highly on what your goal here is. Do you - for whatever reason - have to use the given test and training sets as such? If not, a valid approach would be to shuffle the available data and determine a new test and training set, so that they each contain all classes. Do you want a classifier only for d and e, and have to use ...

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Something like this for instance: library(plyr) ddply(data,'region',function(x) {mean(x$age)}) 1 As Benj said, there's no general answer since it depends not only on the algorithm but also a lot on the data. It's easy to find examples where the exact same size of data with the same algorithm performs terrible in one case and perfectly in the other. Given a particular dataset and a particular algorithm, there are experimental methods which can help ... 2 Any good hash will be uniformly distributed, which means that you can assume a uniform distribution when you apply modulo n, as long as$n < 2^{M/2}$, where M is the number of bits in your hash, see here. So for SHA1-32 you would at most modulo by$2^{16}\$. There is no approach to calculating an integer value; what you have there is an hexadecimal ...

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