Questions tagged [machine-learning]
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
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What is the difference between explainable and interpretable machine learning?
O’Rourke says that explainable ML uses a black box model and explains it afterwards, whereas interpretable ML uses models that are no black boxes.
Christoph Molnar says interpretable ML refers to the ...
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Monitoring machine learning models in production
I am looking for tools that allow me to monitor machine learning models once they are gone to production. I would like to monitor:
Long term changes: changes of distribution in the features with ...
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What is the difference between word-based and char-based text generation RNNs?
While reading about text generation with Recurrent Neural Networks I noticed that some examples were implemented to generate text word by word and others character by character without actually ...
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What are the differences between Convolutional1D, Convolutional2D, and Convolutional3D?
I've been learning about Convolutional Neural Networks. When looking at Keras examples, I came across three different convolution methods. Namely, 1D, 2D & 3D. ...
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Macro- or micro-average for imbalanced class problems
The question of whether to use macro- or micro-averages when the data is imbalanced comes up all the time.
Some googling shows that many bloggers tend to say that micro-average is the preferred way ...
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Could Deep Learning be used to crack encryption?
Say you have a dataset with millions of rows and the attributes Plain Text, Key, and Output Ciphertext. Could Deep Learning, theoretically, be used to find patterns in the outputs that help decipher ...
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How to maximize recall?
I'm a little bit new to machine learning.
I am using a neural network to classify images. There are two possible classes. I am using a Sigmoid activation at the ...
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What is difference between one hot encoding and leave one out encoding?
I am reading a presentation and it recommends not using leave one out encoding, but it is okay with one hot encoding. I thought they both were the same. Can anyone describe what the differences ...
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Advantages of stacking LSTMs?
I'm wondering in what situations it is advantageous to stack LSTMs?
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Interactive labeling/annotating of time series data
I have a data set of time series data. I'm looking for an annotation (or labeling) tool to visualize it and to be able to interactively add labels on it, in order to get annotated data that I can use ...
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One hot encoding alternatives for large categorical values
I have a data frame with large categorical values over 1600 categories. Is there any way I can find alternatives so that I don't have over 1600 columns?
I found this interesting link.
But they are ...
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Merging sparse and dense data in machine learning to improve the performance
I have sparse features which are predictive, also I have some dense features which are also predictive. I need to combine these features together to improve the overall performance of the classifier.
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How many images per class are sufficient for training a CNN
I'm starting a project where the task is to identify sneaker types from images. I'm currently reading into TensorFlow and Torch implementations. My question is: how many images per class are required ...
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XGBoost outputs tend towards the extremes
I am currently using XGBoost for risk prediction, it seems to be doing a good job in the binary classification department but the probability outputs are way off, i.e., changing the value of a feature ...
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Feature Scaling both training and test data
It is stated that for:
Feature Normalization -
The test set must use identical scaling to the training set.
And the point is given that:
Do not scale the training and test sets using different ...
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Can a neural network compute $y = x^2$?
In spirit of the famous Tensorflow Fizz Buzz joke and XOr problem I started to think, if it's possible to design a neural network that implements $y = x^2$ function?
Given some representation of a ...
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Decision tree vs. KNN
In which cases is it better to use a Decision tree and other cases a KNN?
Why use one of them in certain cases? And the other in different cases? (By looking at its functionality, not at the ...
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Bagging vs Dropout in Deep Neural Networks
Bagging is the generation of multiple predictors that works as ensamble as a single predictor. Dropout is a technique that teach to a neural networks to average all possible subnetworks. Looking at ...
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Use liblinear on big data for semantic analysis
I use Libsvm to train data and predict classification on semantic analysis problem. But it has a performance issue on large-scale data, because semantic analysis concerns n-dimension problem.
Last ...
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Updating the weights of the filters in a CNN
I am currently trying to understand the architecture of a CNN. I understand the convolution, the ReLU layer, pooling layer, and fully connected layer. However, I am still confused about the weights.
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With unbalanced class, do I have to use under sampling on my validation/testing datasets?
I’m a beginner in machine learning and I’m facing a situation. I’m working on a Real Time Bidding problem, with the IPinYou dataset and I’m trying to do a click prediction.
The thing is that, as you ...
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Detecting cats visually by means of anomaly detection
I have a hobby project which I am contemplating committing to as a way of increasing my so far limited experience of machine learning. I have taken and completed the Coursera MOOC on the topic. My ...
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Beginner math books for Machine Learning
I'm a Computer Science engineer with no background in statistics or advanced math.
I'm studying the book Python Machine Learning by Raschka and Mirjalili, but when I tried to understand the math of ...
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Prediction interval around LSTM time series forecast
Is there a method to calculate the prediction interval (probability distribution) around a time series forecast from an LSTM (or other recurrent) neural network?
Say, for example, I am predicting 10 ...
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High-dimensional data: What are useful techniques to know?
Due to various curses of dimensionality, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/...
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One-Class discriminatory classification with imbalanced, heterogenous Negative background?
I'm working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not.
There are about 1,150 known ...
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Why should we not feed LDA with TF-IDF input?
Can someone explain why we can not feed LDA topic model with TFIDF? What is wrong with this approach conceptually?
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Why does adding a dropout layer improve deep/machine learning performance, given that dropout suppresses some neurons from the model?
If removing some neurons results in a better performing model, why not use a simpler neural network with fewer layers and fewer neurons in the first place? Why build a bigger, more complicated model ...
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What does it mean when we say most of the points in a hypercube are at the boundary?
If I have a 50 dimensional hypercube. And I define it's boundary by $0<x_j<0.05$ or $0.95<x_j<1$ where $x_j$ is dimension of the hypercube. Then calculating the proportion of points on the ...
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Item based and user based recommendation difference in Mahout
I would like to know how exactly mahout user based and item based recommendation differ from each other.
It defines that
User-based: Recommend items by finding similar users. This is often harder to ...
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How to scale an array of signed integers to range from 0 to 1?
I'm using Brain to train a neural network on a feature set that includes both positive and negative values. But Brain requires input values between 0 and 1. What's the best way to normalize my data?
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Overfitting in Linear Regression
I'm just getting started with machine learning and I have trouble understanding how overfitting can happen in a linear regression model.
Considering we use only 2 feature variables to train a model, ...
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What are the implications for training a Tree Ensemble with highly biased datasets?
I have a highly biased binary dataset - I have 1000x more examples of the negative class than the positive class. I would like to train a Tree Ensemble (like Extra Random Trees or a Random Forest) on ...
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Why are ensembles so unreasonably effective
It seems to have become axiomatic that an ensemble of learners leads to the best possible model results - and it is becoming far rarer, for example, for single models to win competitions such as ...
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Word2Vec embeddings with TF-IDF
When you train the word2vec model (using for instance, gensim) you supply a list of words/sentences. But there does not seem to be a way to specify weights for the words calculated for instance using ...
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How to calculate VC-dimension?
Im studying machine learning, and I would like to know how to calculate VC-dimension.
For example:
$h(x)=\begin{cases} 1 &\mbox{if } a\leq x \leq b \\
0 & \mbox{else } \end{cases} $, with ...
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When to choose linear regression or Decision Tree or Random Forest regression? [closed]
I am working on a project and I am having difficulty in deciding which algorithm to choose for regression. I want to know under what conditions should one choose a <...
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R: machine learning on GPU
Are there any machine learning packages for R that can make use of the GPU to improve training speed (something like theano from the python world)?
I see that there is a package called gputools which ...
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Best way to classify datasets with mixed types of attributes
I would like to know what is the best way to classify a data set composed of mixed types of attributes, for example, textual and numerical. I know I can convert textual to boolean, but the vocabulary ...
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Where in the workflow should we deal with missing data?
I'm building a workflow for creating machine learning models (in my case, using Python's pandas and sklearn packages) from data ...
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How to prepare the varied size input in CNN prediction
I want to make a CNN model in Keras which can be fed images of different sizes. According to other questions, I could understand how to set a model, like ...
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Zero Mean and Unit Variance
I'm studying Data Scaling, and in particular the Standardization method.
I've understood the math behind it, but it's not clear to me why it's important to give the features zero mean and unit ...
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What is the use of [SEP] in paper BERT?
I know that [CLS] means the start of a sentence and [SEP] makes BERT know the second sentence has begun.
However, I have a question.
If I have 2 sentences, which are s1 and s2, and our fine-tuning ...
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What is the difference between feature generation and feature extraction?
Can anybody tell me what the purpose of feature generation is? And why feature space enrichment is needed before classifying an image? Is it a necessary step?
Is there any method to enrich feature ...
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How much data are sufficient to train my machine learning model?
I've been working on machine learning and bioinformatics for a while, and today I had a conversation with a colleague about the main general issues of data mining.
My colleague (who is a machine ...
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Binary classification model for unbalanced data
I have a dataset with the following specifications:
Training dataset with 193,176 samples with 2,821 positives
Test Dataset with 82,887 samples with 673 positives
There are 10 features.
I want to ...
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Why my network needs so many epochs to learn?
I'm working on a relation classification task for natural language processing and I have some questions about the learning process. I implemented a convolutional neural network using PyTorch, and I'm ...
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Back-propagation through max pooling layers
I have a small sub-question to this question.
I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was ...
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How to choose a classifier after cross-validation?
When we do k-fold cross validation, should we just use the classifier that has the highest test accuracy? What is generally the best approach in getting a classifier from cross validation?
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How to specify important attributes?
Assume a set of loosely structured data (e.g. Web tables/Linked Open Data), composed of many data sources. There is no common schema followed by the data and each source can use synonym attributes to ...
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