Questions tagged [supervised-learning]

Supervised learning is a type of machine learning algorithm that learns a mapping function y = f(x) between input variables (x) and output variables (y). The two most common supervised learning tasks are classification and regression.

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How to deal with broad and narrow variance within classes in classification tasks

Let's say I'm doing an animal image classification task (it doesn't have to be image classification - this is just my example), and the training and test data is balanced across classes. The classes ...
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34 views

Probabilistic gold standard vs Deterministic gold standard

I understand that we say something as a gold standard when it involves human intervention/judgement/review. But can someone help me understand what's the difference between probabilistic gold ...
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How to update edge features in a graph using a loss function?

Given a directed, edge attributed graph G, where the edge attribute is a probability value, and a particular node N (with binary features f1 and f2) in G, the algorithm that I want to implement is as ...
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25 views

DL model to assess quality of image

I have an idea but I am not certain that it can be modeled in a DL architecture. Let's say we have images of different qualities based on color patterns and their assessment as labels in a range from ...
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1answer
21 views

Multi-Label Loss function and model training

I'm working on Multi-Label problem i.e output can predict 1 or more label as an output and hence training data also have multiple labels. Somehow I'm not able to map such ML model training. Please ...
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36 views

Retraining EfficientNet on only 2 classes out of 4

EfficientNet model was trained on ~3500 images for a 4-class classification: A, B, C and Neither – with accuracy of 0.985 – by someone else, not me. I'm quite new to ML. So we have this model, and it ...
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1answer
19 views

What is the proper way to use time-series data for classification?

I have a time-series dataset for a classification problem. The data contains brain signals collected via EEG eletrodes along 2 seconds in frequency (Hz). The classes are divided in different files (so ...
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9 views

What is the general way of encoding multiple features in a supervised HMM for Named entity recognition?

It think that the title of the question is self explaining. I know that if I have an annotated training set (in my case for NER), I can easily extract the parameters of my hmm using the data - for ...
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35 views

Techniques for increase random forest classifier accuracy

I build basic model for random forest for predict a class. below mention code which i used. ...
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3answers
40 views

How to apply supervised machine learning when the target label depends on multiple input rows?

The problem is a multi-label classification problem. Now, I know how to train and classify using single row with several attributes. For example, if the dataset looks like the first table from the ...
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10 views

How to find top N neighbors of a datapoint in a cluster sorted in increasing order of distance from that point?

I am doing a clustering exercise and I am doing it using K-Means. After doing the clustering part, I have a dataframe that looks something like this : ...
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139 views

Document similarity

I have close to 50000 documents in plain text format. Is there a way in which I can group similar documents together? Similarity mostly here is the content similarity. Will transforming the text ...
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1answer
30 views

Machine learning analysis for data set

I have a data set that contains houses, different features, and its prices. I'm trying to do an advanced analysis for this data set, I already did house price prediction analysis using different ...
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21 views

Knowing Joint probability distribution between feature-label space

I am doing a course CORNELL CS4780 "Machine Learning for Intelligent Systems". you can find the link here for the one I am going to refer 1st lecture The professor explains, we have a sample $D ={...
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From unsupervised to supervised in fraud detection

I have a question. I am working on the fraud detection domain. And I have data from imports to the country. As you can get from the title, I have unsupervised data. I do not know that the record is ...
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What would I prefer - an over-fitted model or a less accurate model?

Let's say we have two models trained. And let's say we are looking for good accuracy. The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an ...
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1answer
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When unsupervised learning is more beneficial in comparison with supervised learning even the labelings are existed?

When unsupervised learning is more beneficial in comparison with supervised learning even the labeling are existed? If there is no labeling the unsupervised learning is better than supervised learning ...
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2answers
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Classification when variables are in ranges

I want to classify my data and some of my variables are ranges. I classify location so for example, school, the hours that people are at school are from 7:00 to 14:00, some of my variables are ...
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Interaction with unseen data (Generalization and evaluation the performance on unseen data in supervised and unsupevised learning methods)

How to generalizes model and performs on unseen data for a highly imbalanced binary classification problem (99.827%,0.173%)? 1-When using supervised learning methods such as logistic reg, RFs, ...
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64 views

Can we apply to GridSearchCV to Logistic regression .?

When I apply GridSearchCV to my model Logistic Regression, it's continuously throwing below error. I understand that it's trying to convert string to float. But that's was my data. So how can I ...
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28 views

Is pattern recognition the same as unsupervised learning? Is machine learning the same as supervised learning?

Firstly, here is the definition of a well-posed learning problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its ...
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1answer
26 views

What is the difference between all the different types of learning within machine learning?

This is a question that is really hard to google, and the differences are confusing. Does anyone have good examples of the differences between them all? Supervised Learning Semi-Supervised Learning ...
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55 views

How to apply Machine learning model on time series to predict next time step

I have done feature engineering on a single variable time series data (spare parts usage), then I turn the time series data into supervised machine learning problem. I have trained and test on the ...
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23 views

2nd, 3rd, Nth closest guesses

I have used the KMeans algorithm to create an engine that can guess the cluster that a particular set of input data will fall into. Can I use it to guess the 2nd closest cluster, 3rd closest, and so ...
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What is the approx minimum size of dataset required to build 90% correct model?

I am working with a financial dataset size which is around 3000. I have attempted the supervised-learning regression techniques and not able to go beyond 70% accuracy. Features: 10 Data size:3700 ...
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1answer
52 views

Time Series Generation - Multi Dimensional Time Series Data

Disclaimer: Mathematicians please don't be mad at me for the use of some of the terminologies in this post. I am an Engineer. :-) Background: So I am currently working on a problem where I have to ...
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Mixing unsupervised and supervised algorithms in image classification model

I am trying to replicate the general image classification model used in a paper that I cite later below. The following image is an extract from a paper that proposes a novel method of performing image ...
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2answers
37 views

Is it a best practice to exclude retweets from the data set?

I am going to build machine learning algorithm to identify fake tweets. The data set has huge retweets which I think might be an issue. Do you think given that the focus is the original tweet, it is ...
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51 views

Reshape Time series data for Conv2d Block

I am modelling my time series data into a supervised learning problem for the input to a conv2d block in pytorch from this tutorial. ...
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29 views

Extracting Features for Graph transformation

Suppose I have a directed graph G (V,E) whose transformation is defined by a library of patterns. Each vertex is of particular type. The library of patterns contain subgraphs (g1,g2,g3 etc)and it's ...
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1answer
37 views

Doing predictive modeling on predicted value

It's a project that I'm working on. Here are the steps I took: I want to make a recommendation service based on the customer data. I first used a collaborative filtering method to get the recommended ...
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2answers
159 views

Suggestion for stacked modelling in machine learning

I have built several models on the training dataset and i am not happy with the results and I wish to club them all together and generate a new model, so here is my idea as i already have the results ...
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21 views

Underfitting: Kernelizing and Optimazing

Today I was at a research conference and I have question what is meant with this: What does it mean to better optimize a supervised classification machine learning algorithm in combination with ...
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Is it wise to include the target labels when a supervised learning problem is tackled as an unsupervised learning problem?

I have a problem which requires both a supervised and unsupervised learning approach. This is because I am trying to find some hidden clusters (if they even exist) beyond the labels of the dataset. ...
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33 views

Predict the mistakes of my Model?

This is in the context of trading software: Let's say I am party A that is analyzing historical data of stock shares. Based on these historical data I want to decide whether I should buy a stock or ...
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1answer
16 views

``Hierarchial features extraction'' in Multilayer Perceptron models

I am referring to plain neural networks, MLPs. I got to read the paper by Glorot and Bengio (2010), Understanding the difficulty of training deep feedforward neural networks. Therein I read an ...
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28 views

Dataset Image creation suggestions

I am trying to create a dataset where the Text is mixed, e.g. " I love football" can be written as " I l()v3 F00tba11". The idea is that by using Tesseract I can find the pattern to match these two ...
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4answers
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Supervised clustering

I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised ...
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1answer
23 views

Supervised Learning: time estimation of bike repair

I would like to train a model that estimate the time a given shop would take to repair for a bike using the data below: shops.csv ...
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2answers
48 views

How to train a Machine Learning model for blocked data

I'm concerned with a supervised classification problem for the following type of data. The data consists of $N$ rows (where $N$ is not very large - this is not a big-data problem) and $M$ columns (...
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7 views

How to check if a class segmentation is meaninfull?

Using the Lending Club dataset I have a data frame with the loan characteristics of some borrowers. Here is the distribution of the subgrades: ...
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2answers
177 views

How to represent audio data in a format that can be used for preprocessing and modelling?

I have a project that I am working on currently. The project is to classify audio data. The data is in two folders train and test...
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1answer
13 views

adding non-failure data to failure one

I have a dataset containing features of different engines showing when they failed. I want to build supervise learning model to predict whether an engine with a certain mileage is going to fail or not....
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3answers
144 views

Why models performs better If normalize test data and train data separately?

Many threads (and courses) such as this and this one suggest that you should apply normalization to the test data using the parameters used in the training set. But other some discussions I've found ...
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2answers
120 views

Why is there a trade-off between bias and variance in supervised learning? Why can't we have best of both worlds?

The bias-variance trade-off is like a law in machine learning. You cannot have the best of both worlds. What is it about supervised learning in machine learning that makes it impossible to satisfy the ...
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1answer
613 views

svm.LinearSVC: larger max_iter number doesn't always increase the accuracy/precision/recall

Background: Supervised machine learning Data shape 10+ features, target = 1 or 0 only, 100,000+ samples (so should be no issue of over-sampling) 80% training, 20% testing train_test_split(X_train, ...
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Using Unsupervised / Supervised Learning for RGB manipulation

I need to teach a model(or any substitute) for automatically adjusting the concentration of R, G, and B values until they make up white colour. i.e, tweak the RGB values (beginning from some arbitrary ...
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1answer
28 views

Predicting probability for each tag given already chosen tags

I have a set of tags (~10'000, will be extended over time) presented to a user. After he has selected 3 or more tags, I want to predict for each remaining tag what the chances are that the user will ...
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1answer
20 views

Very simple real-valued time-series dataset for RNN prototyping

Is there a simple real-valued time-series dataset on which a vanilla RNN model can be trained. With "very simple" I mean only two to four real-valued inputs per time step and a single real-valued ...
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Is there a way to use Plotly as an annotation tool, for labelling time-series for instance?

I have been tasked to create a tool aimed at labelling sections and/or precise data points of a biomedical time-series. Our main framework is written in Python. I would like to know whether it is ...

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