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 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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22 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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32 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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27 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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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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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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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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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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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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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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``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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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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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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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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23 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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Scikit learn - best model to classify supervised two-feature data?

I'm quite new to scikit learn but I am looking for the best approach to go about classifying some data I've collected where each set contains two measurements made over several points of time, along ...
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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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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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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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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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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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154 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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25 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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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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22 views

Algorithm for user profiling without distinct profiles

I am trying to design an algorithm that takes in a new user with the variables department, location, job_role etc. and I want a machine-learning algorithm to decide ...
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41 views

How do ML model retain/store the learning(s)?

In other words, if the model after training and testing is ready for making future predictions, it must be storing the learning(s) somewhere in memory or disk or cache (or I really don't know). So, ...
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509 views

XGBOOST : model.predict_proba() and model.predict() conflicting behaviour

I have two classes : 1 and 2 The output of model.predict_proba() -> [0.333,0.6667] The output of model.predict() -> 1 This is happening for around 200 test values out of the test data of 10 lac. ...
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45 views

Super Resolution CNN vs Regular CNN

I am digging into finding a solution for background subtraction and one of the requirements is to not loose in quality of input image. Found that there is a specific type of CNN like Super Resolution ...
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suitable algorithms for very high dimensional ”binary pattern" recognition?

I have a dataset consisting of 3000 binary features and one binary ouput. subset of these binary features form binary patterns. these subsets could be neighbouring features or from different regions (...
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Which feature to use in feature selection?

Objective: Multiclass classification with supervised learning, small dataset (25h) Context: My dataset is composed of mobile network data collected with a smartphone. The labels correspond to the ...
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Supervised Learning Quiz website?

What are some good websites which provide quiz questions on supervised learning and machine learning in general? I have a quiz coming up and I would like to be prepared for it.
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Why does feature scaling improve the convergence speed for gradient descent?

From this article, it says: We can speed up gradient descent by scaling. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down ...
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Extract data from mainly unstructured sets and derive risk metrics out of those

I have the following question (this was a real life example): Q: Extract data from mainly unstructured sets and derive risk metrics out of those. From what you know or imagine about the data ...
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How to use fresh data when target prediction period is long?

I'm using supervised learning on monthly activity data to predict when a customer buys a particular product. This product is typically bought infrequently and at the moment my target variable is ...
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36 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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How do I use the supervised learning classification in my project? [closed]

Just to give an idea of what I'm doing: I'm doing a project with financial data (tick data) and I'm trying to create a way make a model learn what happens before a breakout. Usually there ...
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Supervised learning approach - creating my own labels

Scenario - I have data that does not have labels but I can create a function to label the data based on behavior and deploy the model so I don't have to keep labeling the data. Is this considered ...
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How to efficiently iterate a supervised model over the Cartesian product of very large number of records?

The problem: Two large databases, with ~1M records each, "old customer data" and "new customer data". The data came from different sources and was ingested at different times, so there are many ...
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56 views

Is conditional GAN supervised learning?

I am trying to understand this paper about conditional GAN, it says that extra information y (class labels) is given to the network. However, I cannot understand its usage during training or its ...
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1answer
53 views

Methodology for driving score(behavior)

I am an intern at mobility data company and a Master's candidate in Statistics. I am researching about driving score which is based on a driver's driving habit. We have trip data which contains the ...
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Which metric should be used to select best binary pixel-wise classifier for segmentation task?

I am doing a semantic segmentation task using a supervised algorithm to classify image pixels into one class or the other (binary classification). I am trying several classifiers and feature ...
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249 views

How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)

I came through this questions and I failed to find the right answer for it. How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)?...
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Proportion of positive/negative label in Supervised Learning

I'm working on a Supervised Machine Learning problem and I have a question about the proportion of positive/negative label. I would like to categorize some batch as OK or NOK. But actually my batchs ...
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semi supervised learning using transfer learning and shared memory

I am reading a paper here and I am not sure I am understanding something. They claim to have 83% unsupervised on CIFAR 10, but they used something that is semi supervised. At the very least, they used ...
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When is the sum of models the model of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...