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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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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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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independent training algorithms for neural network in Matlab

The picture below shows some of training algorithms in Matlab used to train a neural network. These algorithms can be classified into 3 main families: Gradient Descent, Conjugate Gradient Descent and ...
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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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How to create Independent Neural Networks by changing the architecture?

I am trying to build 3 independent Neural Networks that are trained on the same dataset and fuse the NNs using a voting system. I came to the conclusion that the effectiveness of the voting system is ...
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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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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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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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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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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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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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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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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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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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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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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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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 ...
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What kind of learning in this training situation when anomaly detection? Supervised learning,semi-supervised learning or unsupervised learning?

I am doing anomaly detection recently, one of the methods is using AEs model to learn the pattern of normal samples. Determine it as an abnormal sample if it doesn’t match the pattern of normal ...
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How to label a dataset for Machine Learning?

I have a collection of educational dataset. The dataset consists of a username and their review for the course. I want to analyze the data for sentiment analysis. How can I label the data to train ...
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Unsupervised clustering improved with supervised classification accuracy

I have a set of labeled samples each containing up to 300 different objects. For every object I have a set of features describing the object. For example, Sample with label '1': 50 objects of type ...
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Liner regression and feature scaling

Below are few questions where I unable to find out where I am wrong. I added screen shot of image and explanations of the each options that I am understanding. Questions are purely discussion based ...
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Balanced vs total dataset rows, which one is better?

I work on a dataset concerning games playing results. i.e every child play an indefinite number of games and it has as output (y) two possible values "success" or "Failure". It's about 800 000 ...
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Philosophical question on redundancy

Suppose I implement a supervised learning version of LSTM similar to this. Namely, I have these univariate time series data: ...
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which metric is better for boosting methods

I work on a dataset of 300 000 samples and I try to make a comparison between logistic regression (with gradients descent) and a LightBoost for binary classification in order to choose the better one. ...
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Labeling classes conditionally

I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user. Initially I used binary classes: labeled 1 for next ...
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Supervised learning for a turn-based game?

So I have 4GB of turn-by-turn data for many games of a particular strategy game. It appears that most people interested in using ML to build an AI for turn-based games use reinforcement learning to ...