I would like to use the MNIST dataset, where each digit is assigned a specific colour. Not the background, the digit itself.
The following dataset colours the background of the image: https://www.wouterbulten.nl/blog/tech/getting-started-with-gans-2-colorful-mnist/
Maybe you are looking for the coloured MNIST dataset?
I have seen two papers proposing it:
Invariant Risk Minimisation: source code to generate the data
PREDICTING WITH HIGH CORRELATION FEATURES: source code to generate
the data
Related
I am working on a text classification use case. The text is basically contents of legal documents, for example, companies annual reports, W9 etc. So there are 10 different categories and 500 documents in total. Therefore 50 documents per category. So the dataset consists of 500 rows and 2 columns, 1st column consisting of text and 2nd column is the Target.
I have built a basic model using TF-IDF for my textual features. I have used Multinomial Naive Bayes, SVC, Linear SGD, Multilayer Perceptron, Random Forest. These models are giving me an F1-score of approx 70-75%.
I wanted to see if creating word-embedding will help me improve the accuracy. I trained the word vectors using gensim Word2vec, and fit the word vectors through the same ML models as above, but I am getting a score of about 30-35%. I have a very small dataset and lot of categories, is that the problem? Is it the only reason, or there is something I am missing out?
I am using Weka IBk for text classificaiton. Each document basically is a short sentence. The training dataset contains 15,000 documents. While testing, I can see that k=1 gives the best accuracy? How can this be explained?
If you are querying your learner with the same dataset you have trained on with k=1, the output values should be perfect barring you have data with the same parameters that have different outcome values. Do some reading on overfitting as it applies to KNN learners.
In the case where you are querying with the same dataset as you trained with, the query will come in for each learner with some given parameter values. Because that point exists in the learner from the dataset you trained with, the learner will match that training point as closest to the parameter values and therefore output whatever Y value existed for that training point, which in this case is the same as the point you queried with.
The possibilities are:
The data training with data tests are the same data
Data tests have high similarity with the training data
The boundaries between classes are very clear
The optimal value for K is depends on the data. In general, the value of k may reduce the effect of noise on the classification, but makes the boundaries between each classification becomes more blurred.
If your result variable contains values of 0 or 1 - then make sure you are using as.factor, otherwise it might be interpreting the data as continuous.
Accuracy is generally calculated for the points that are not in training dataset that is unseen data points because if you calculate the accuracy for unseen values (values not in training dataset), you can claim that my model's accuracy is the accuracy that is been calculated for the unseen values.
If you calculate accuracy for training dataset, KNN with k=1, you get 100% as the values are already seen by the model and a rough decision boundary is formed for k=1. When you calculate the accuracy for the unseen data it performs really bad that is the training error would be very low but the actual error would be very high. So it would be better if you choose an optimal k. To choose an optimal k you should be plotting a graph between error and k value for the unseen data that is the test data, now you should choose the value of the where the error is lowest.
To answer your question now,
1) you might have taken the entire dataset as train data set and would have chosen a subpart of the dataset as the test dataset.
(or)
2) you might have taken accuracy for the training dataset.
If these two are not the cases than please check the accuracy values for higher k, you will get even better accuracy for k>1 for the unseen data or the test data.
I have a data with some categorical variables and I want to run a logistic regression using Mllib , it seems like the model support only continous variables.
Does anyone know how to deal with this please ?
Logistic regression, like the other linear models, takes as input an RDD whereas a LabeledPoint is a Double (the label) and the associated Vector (a double Array).
Categorical values (Strings) are not supported, however you could convert those to binary columns.
For example if you have a column RAG taking values Red, Amber and Green, you would add three binary column isRed, isAmber and isGreen of which only one of them is 1 (true) and the others are 0 (zero) for each sample.
See as further explanation: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html
There is a module named SMOTE(Synthetic Minority Oversampling Technique ) which increase the number of samples of under sampled data, I guess we should choose a feature(feature to be predicted) which is under represented. How to choose it? There seems to be no option on choosing the coloumn.
I guess you are referring to the target variable (label column). You can set that using a Metadata Editor module. Choose your label column using the column selector and set the Fields property to Labels.
Here is the SMOTE definition - SMOTE is an approach for the construction of classifiers from imbalanced datasets, which is when classification categories are not approximately equally represented. The classification category is the feature that the classifier is trying to learn. There is not an option of choosing the column in the SMOTE module because it should be the label column
Here is the details on how to use SMOTE in Azure Machine Learning - https://msdn.microsoft.com/en-us/library/azure/dn913076.aspx?f=255&MSPPError=-2147217396
You can do it thru the column selector. In the sample below, the blood donation data (a sample dataset in Azure ML) has 25% of people who donated (class 1).
Can someone share a code snippet that shows how to use SVM for text mining using scikit. I have seen an example of SVM on numerical data but not quite sure how to deal with text. I looked at http://scikit-learn.org/stable/auto_examples/document_classification_20newsgroups.html
but couldn't find SVM.
In text mining problems, text is represented by numeric values. Each feature represent a word and values are binary numbers. That gives a matrix with lots of zeros and a few 1s which means that the corresponding words exist in the text. Words can be given some weights according to their frequency or some other criteria. Then you get some real numbers instead of 0 and 1.
After converting the dataset to numerical values you can use this example: http://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC