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 active learning and reinforcement learning?
From Wikipedia:
Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the ...
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What is one hot encoding in tensorflow?
I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.
Can somebody please explain to ...
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Why should we use (or not) dropout on the input layer?
People generally avoid using dropout at the input layer itself. But wouldn't it be better to use it?
Adding dropout (given that it's randomized it will probably end up acting like another regularizer)...
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Is feature selection necessary?
I would like to run some machine learning model like random forest, gradient boosting, or SVM on my dataset. There are more than 200 predictor variables in my dataset and my target classes are a ...
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LightGBM gives different results (metrics) depending on the columns order
I have two nearly identical datasets A and B which differ only in terms of columns ordering. I then train a LightGBM model on each of the two datasets with the following steps:
Divide each dataset ...
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Is there a person class in ImageNet? Are there any classes related to humans?
If I look at one of the many sources for the Imagenet classes on the Internet I cannot find a single class related to human beings (and no, harvestman is not someone who harvests, but it's what I knew ...
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Visualizing deep neural network training
I'm trying to find an equivalent of Hinton Diagrams for multilayer networks to plot the weights during training.
The trained network is somewhat similar to a Deep SRN, i.e. it has a high number of ...
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Is stratified sampling necessary (random forest, Python)?
I use Python to run a random forest model on my imbalanced dataset (the target variable was a binary class). When splitting the training and testing dataset, I struggled whether to used stratified ...
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Is there any APIs for crawling abstract of paper?
If I have a very long list of paper names, how could I get abstract of these papers from internet or any database?
The paper names are like "Assessment of Utility in Web Mining for the Domain of ...
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Predict the best time of call
I have a dataset including a set of customers in different cities of California, time of calling for each customer, and the status of call (True if customer answers the call and False if customer does ...
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Multi task learning in Keras
I am trying to implement shared layers in Keras. I do see that Keras has keras.layers.concatenate, but I am unsure from documentation about its use. Can I use it to ...
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Why does frequency encoding work?
Frequency encoding is a widely used technique in Kaggle competitions, and many times proves to be a very reasonable way of dealing with categorical features with high cardinality. I really don't ...
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Using attributes to classify/cluster user profiles
I have a dataset of users purchasing products from a website.
The attributes I have are user id, region(state) of the user, the categories id of product, keywords id of product, keywords id of ...
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How can I appropriately handle cleaning of gender data?
I’m a data science student and I’ve begun working with an open mental health dataset. As part of this, I need to clean the data so that I can perform an analysis of it.
In this dataset, the gender ...
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Is PCA considered a machine learning algorithm
I've understood that principal component analysis is a dimensionality reduction technique i.e. given 10 input features, it will produce a smaller number of independent features that are orthogonal and ...
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Looking for example infrastructure stacks/workflows/pipelines
I'm trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used ...
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Do you actually need math for your data science job?
I am a physicist working in a data scientist role. I was told everywhere that my degree is a very good starting point because I know a lot of math and it is crucial for this job. But other than ...
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When to remove correlated variables
Can somebody please suggest what is the correct stage to remove correlated variables before feature engineering or after feature engineering ?
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How does the naive Bayes classifier handle missing data in training?
Naive Bayes apparently handles missing data differently, depending on whether they exist in training or testing/classification instances.
When classifying instances, the attribute with the missing ...
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Studying machine learning algorithms: depth of understanding vs. number of algorithms
Recently I was introduced to the field of Data Science (its been 6 months approx), and Ii started the journey with Machine Learning Course by Andrew Ng and post that started working on the Data ...
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Keras Multiple “Softmax” in last layer possible?
Is it possible to implement mutiple softmaxes in the last layer in Keras? So the sum of Nodes 1-4 = 1; 5-8 = 1; etc.
Should I go for a different network design?
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What is the difference between Dilated Convolution and Deconvolution?
These two convolution operations are very common in deep learning right now.
I read about dilated convolutional layer in this paper : WAVENET: A GENERATIVE MODEL FOR RAW AUDIO
and De-convolution is ...
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How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries
Experts in my field are capable of predicting the likelyhood an event (binary spike in yellow) 30 minutes before it occurs. Frequency here is 1 sec, this view represents a few hours worth of data, i ...
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Input normalization for ReLu?
Let's assume a vanilla MLP for classification with a given activation function for hidden layers.
I know it is a known best practice to normalize the input of the network between 0 and 1 if sigmoid ...
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Stratify on regression
I have worked in classification problems, and stratified cross-validation is one of the most useful and simple techniques I've found. In that case, what it means is to build a training and validation ...
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How to Predict the future values of time horizon with Keras?
I just built this LSTM neural network with Keras
...
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Validation loss and accuracy remain constant
I am trying to implement this paper on a set of medical images. I am doing it in Keras. The network essentially consists of 4 conv and max-pool layers followed by a fully connected layer and soft max ...
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What to do when testing data has less features than training data?
Let's say we are predicting the sales of a shop and my training data has two sets of features:
One about the store sales with the dates (the field "Store" is not unique)
One about the store types (...
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Machine learning libraries for Ruby
Are there any machine learning libraries for Ruby that are relatively complete (including a wide variety of algorithms for supervised and unsupervised learning), robustly tested, and well-documented? ...
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Sentiment data for Emoji
For experimenting we'd like to use the Emoji embedded in many Tweets as a ground truth/training data for simple quantitative senitment analysis. Tweets are usually too unstructured for NLP to work ...
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Is Gradient Descent central to every optimizer?
I want to know whether Gradient descent is the main algorithm used in optimizers like Adam, Adagrad, RMSProp and several other optimizers.
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Difference between machine learning and artificial intelligence
Is there any difference between machine learning and artificial intelligence? Or do these terms refer to the same thing?
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Clustering with cosine similarity
I have a large data set and a cosine similarity between them. I would like to cluster them using cosine similarity that puts similar objects together without needing to specify beforehand the number ...
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Best Julia library for neural networks
I have been using this library for basic neural network construction and analysis.
However, it does not have support for building multi-layered neural networks, etc.
So, I would like to know of any ...
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Unstructured text classification
I'm going to classify unstructured text documents, namely web sites of unknown structure. The number of classes to which I am classifying is limited (at this point, I believe there is no more than ...
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How does Sigmoid activation work in multi-class classification problems
I know that for a problem with multiple classes we usually use softmax, but can we also use sigmoid? I have tried to implement digit classification with sigmoid at the output layer, it works. What I ...
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Algorithm for generating classification rules
So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "...
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1% of data for training 99% of data for testing
I got feedback from a reviewer. It is really important for me to answer to this question. I would appreciate of any help.
it was mentioned that 1% of the data was used for training while 99% was used ...
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How to measure the similarity between two images?
I have two group images for cat and dog. And each group contain 2000 images for cat and dog respectively.
My goal is try to cluster the images by using k-means.
Assume image1 is ...
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How can I do classification with categorical data which is not fixed?
I have a classification problem with both categorical and numerical data. The problem I'm facing is that my categorical data is not fixed, that means that the new candidate whose label I want to ...
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Train new data to pre-trained model
Let's say I've trained my model and made my predictions.
My question is... How can I append some new data to my pre-trained model without retrain the model from the beginning.
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How to do stepwise regression using sklearn? [duplicate]
I could not find a way to stepwise regression in scikit learn. I have checked all other posts on Stack Exchange on this topic. Answers to all of them suggests using f_regression.
But f_regression ...
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Large Graphs: NetworkX distributed alternative
I have built some implementations using NetworkX(graph Python module) native algorithms in which I output some attributes which I use them for classification ...
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Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?
I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting.
But I am confused that which ...
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Can we take of benefit of using transfer learning while training a word2vec models?
I am looking to find a pre-trained weights of an already trained models like Google News data etc. I found it hard to train a new model with enough amount (10 GB etc) of data for myself. So, I want to ...
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How does generalised advantage estimation work?
I've been trying to add GAE to my A2C implementation for a while now, but I' can't quite seem to grok how it works.
My understanding of it, is that it reduces the variance of the advantage estimation ...
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Activation function between LSTM layers
I'm aware the LSTM cell uses both sigmoid and tanh activation functions internally, however when creating a stacked LSTM architecture does it make sense to pass their outputs through an activation ...
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Interpreting the Root Mean Squared Error (RMSE)!
I read all about pros and cons of RMSE vs. other absolute errors namely mean absolute error (MAE). See the the following references:
MAE and RMSE — Which Metric is Better?
What's the bottom line? How ...
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CNN memory consumption
I'd like to be able to estimate whether a proposed model is small enough to be trained on a GPU with a given amount of memory
If I have a simple CNN architecture like this:
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What happens when we train a linear SVM on non-linearly separable data?
What happens when we train a basic support vector machine (linear kernel and no soft-margin) on non-linearly separable data? The optimisation problem is not feasible, so what does the minimisation ...
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