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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19 views

Difference between self supervised learning and unsupervised learning

Self supervised learning is considered a subset of unsupervised learning. Is there any major difference between the two owing to the similarity of self supervised methods towards supervised learning.
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how to correct mislabeled data in training, validation and test set

In an image classification task, I know there are mislabeled data. should I remove/correct them in all training / validation / test set ? I saw this article https://arxiv.org/pdf/2103.14749.pdf but I ...
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38 views

Does knn extend the train dataset by test values during the prediction?

Lets say I have 100 values in my dataset and split it 80% train 20% test. When predicting the last value, is the prediction based on previous 99 (80 test + 19 already predicted values) or only the ...
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What is the name of this supervised clustering algorithm?

I am doing deep learning research in supervised contrastive learning. The problem I am interested in can be simplified into the below scenario. And I am wondering what is the name of the following ...
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26 views

Is Cross validation and GridSearchCV required every time we train a model?

I have a repetitive process that will build a model weekly based on the previous week's data. So while in development I tried GridSearchCV and cross-validation to ...
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17 views

Should I normalise my data if future unseen data may have a different range?

I'm new to ML and researching data prep, more specifically feature normalisation. My question is whether it's a good idea to normalise data when its range may change over time? For example, if I'm ...
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Extracting Keywoards from messages with own NER Model

I'm starting a project where I want to extract keywoards from given messages. The keywoards are for example something like: "hard disk", "watch" or other technical components. I'm ...
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Looking for papers within particular deep learning domain

I have labels for training data e.g. regression / classification on top of it I have some supplementary information only for the training data that shows more detailed information on the labels (how ...
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25 views

Why is HistGradientBoostingRegressor in sklearn so fast and low on memory?

I trained multiple models for my problem and most ensemble algorithms resulted in lengthy fit and train time and huge model size on disk (approx 10GB for RandomForest) but when I tried ...
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RandomForestRegressor can't predict border extreme low and extreme high values

I need to predict the average check for supermarkets and I have a dataset (745, 25). I fitted the model by RandomForestRegressor. Firstly, The data has log-normal distribution with positively skewed. ...
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126 views

Where and how to do large scale supervised machine learning?

I'm beginner in ML and I have a large dataset that has 15 features with 6M rows, so it becomes challenging to work on it locally. I can train one model locally but to perform hyper parameter tuning ...
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30 views

regression with noisy target vairable

How can I approach a regression problem where the input data is not noisy but the target variable is noisy? Are there any regression algorithms that are robust to a noisy target variable? Also, is it ...
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Doing Supervised Learning where output should be a probability that adds to 1

I'm trying to do some supervised learning on a dataset to surface which input is the most probable true input Example Dataset As you can see, each INPUT_ID only has ...
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45 views

Convert a binary neural network classifier to one verses all classifier

I have a neural network model (implemented from scratch) which gives me some continuous outputs and I have used a sigmoid layer, in the end, to convert it into a binary classifier. But my original ...
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18 views

Create new performance indicators (error metrics)

I am wondering if any of you happen to know of a procedure/approach/rationale to develop new performance indicators (error metrics) that can be used to evaluate the prediction capability (say, ...
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How to correctly train CNN with patches taken by labeled images, if the source image contains both positive and negative samples?

I have patches (tiles) taken from very large histopathological images. These images are labeled as healthy (negative) or tumor (positive). If the image-level label is negative, then 100% of the source ...
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43 views

Training a model purely on weak labels

I have read a couple of papers now use rules-based system to create weak labels and then train a BERT-based model only using these weak labels. Both studies have reported better performances on ...
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21 views

Different learning curves on each run

Sorry for the wrong terminology I might use, since I’m a noob. For my supervised learning project for the university I have a dataset (features and labels) which has to evaluated in several ways and ...
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Link Prediction in Directed Social Network using Supervised Learning

I am trying to solve the link prediction problem in a directed graph using supervised learning. In the case of an undirected graph, it is pretty straightforward. For instance, first, compute the ...
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Relation between number of landmarks and learning accuracy

Is there any relation between number of landmarks selected on the subject and the accuracy of learning these landmarks? For example for detecting nose tip and eye corners, can we say that adding some ...
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How to do encode this target vector containing strings

Consider a target vector like ...
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12 views

Classification based on color clustering

I need to classify some domain specific images by analysing their color distribution. I have annotated data; this last classification step is supervised. After some preprocessing and masking and other ...
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28 views

Binary classification algorithm where the input variables are arrays

For a project, I'm trying to predict leaks in a network. The network consists of nodes connected by links. What I have are several 'scenarios' where each scenario has a leak present at a different ...
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At what correlation factor we consider two features highly correlated so that it is safe to drop one feature for supervised learning?

At what correlation factor we consider two features highly correlated so that it is safe to drop one feature for supervised learning? It is said that in supervised learning we should remove the high ...
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23 views

URL classification problem with small training set

I have a set of a few thousand organisations (org) with some attributes (name, location, and type) and I want to identify the official URL for each organisation from a set of 10 URLs retrieved from ...
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26 views

What is the best method to *classify* series of data?

I'm working on a project for CCG (such as HearthStone, Yu-Gi-Oh!, etc.) which does classify (or give labels) deck type when user ...
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Predictive Clustering in R with Mixed Data

Does anyone know of a predictive clustering procedure in R for mixed data types? For example, suppose that I had conducted an unsupervised k-prototypes procedure in order to estimate clusters for ...
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30 views

Training a Supervised Classifier using Continuous Data

I am trying to build a NLP classification model using methods such as XGBoost, SVM, logistic regression. The features I am trying to include are cosine similarity and LDA topic models, all of which ...
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How to train a model to predict if 2 samples refer to the same thing?

I have 2 ddbb with around 60.000 samples each. Both have the same features (same column names) that represent particular things with text or categories (turned into numbers). Each sample in a ddbb is ...
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43 views

Rate of convergence - comparison of supervised ML methods

I am working on a project with sparse labelled datasets, and am looking for references regarding the rate of convergence of different supervised ML techniques with respect to dataset size. I know that ...
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102 views

How to use GridSearch for LinearSVC / Random Forest with time series data

I have a question related on how to use the GridSearch to find the best models for my problem with time series data. Every 3 rows is 1 one row in the original dataset. To make my time series problem a ...
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23 views

Semantic segmentation in high-resolution images with high variance - cannot avoid underfitting

I am working on a dataset of 2K images for a semantic segmentation problem. I want to detect and localize small objects, with the smallest mask to be 5x5 pixels. The images include 5 different ...
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72 views

Convert time series data to supervised learning problem

I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each. I am trying to frame this time series problem as a ...
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regarding the inclusion of missclassified data in final training

I find it best to ask my question in terms of cross-validation. Here it goes: Suppose a binary classification problem, for which cross-validation has been applied for a certain learning algorithm. Let'...
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24 views

Definition of weak and strong learners

What is the exact definition, or at least the main idea, of weak and strong learners? For instance, these terms are used in propositions such as "Boosting is based on weak learners". ...
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List of major ML algorithms with code

Can anyone recommend me any GitHub code repo/s or textbook/s that walks through all the major ML algorithms in great detail with code to get hands-on with? I have a data engineering background and ...
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135 views

Understanding Learning Curves

I would like to clarify my understanding of learning curves with two example plots below. I am experimenting with small data sets here between 500 and 1500 samples to clarify my understanding. My ...
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43 views

Self Organizing Map (SOM)

How do you use SOM as a supervised learning technique? Which approach can be added to SOM to turn it to supervised learning?
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42 views

Machine learning and time-based data

I want to predict conversion rates for an eCommerce store. I have data from Google Analytics with features like averageSessionDuration, bounceRate, numberOfVisitorsBySource etc. and the corresponding ...
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32 views

Which supervised ML model to use for exam/grade prediction?

So I plan on making a mobile app that will let students predict their final grades based on their mock exam results. I can train my model with previous years results. X: 5 mock results Y: Final grade ...
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Multiple input single output supervised learning ANN problem

I have a dataset of 120 tuples giving a singular output. I want to implement ANN in estimating the input which is affecting the output most. A case of optimising the input to maximise the output. ANN ...
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1answer
23 views

Standarizing the Label to postive intergers in Machine Learning Classification task, why is it recommended?

When doing a classification task suppose we have 3 targets with notation -2, -1, 0. I read somewhere it is a good practice to Standardize the labels to positive integers.. In this case suppose we ...
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18 views

Should I normalize the dependent variable in a penalized linear regression model?

When I compute penalized regression on the data without normalizing using the glmnet package in R, the lambda values and RMSE generated in lasso, ridge, and elastic net are unreasonably large. The ...
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41 views

Using GANs to generate synthetic tabular data to improve supervised learning

One topic I see some people trying is using GANs to generate synthetic tabular data for supervised learning. Also as a way to oversample the minority class in a binary classification. For me creating ...
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22 views

How to ouput buckets of probabilities?

I am dealing with an unbalanced binary classification problem. The problem is so unbalanced (2:98) and hard to predict that I am interested in probability of the positive outcome instead of trying to ...
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41 views

How to handle One Hot Encoded columns with changing categories in supervised ML Problem?

Scenario: I have the following game data about participants, game and the winner in the following format: ...
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32 views

Classification Model and Their Accuracy [closed]

I am trying to work with Classification model. I am planning to train and test my model with a large dataset (Training with 80% and testing with 20% volume - no under/oversampling). What I understand ...
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SGD converges at exactly the same val accuracy for differnet NN model architecture

Context: Supervised learning binary classification task with input vector of length 136. Using MLP with Dense layers with variations in number of layers, neurons, learning rate, l1, l2 regularization, ...
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How is a policy expressed? [closed]

In my work in behavioural cloning, I have been asked 'how is your policy expressed?' and I didn't know the answer to this. I was trying to create apply a behavioural cloning algorithm from the context ...
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Matching financial reconciliation data / matching multiple rows based on column values

I'm working with financial reconciliation data and the ask is to train the algorithm to match transactions (that are otherwise manually matched if the existing application didn't because not all the ...

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