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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Supervised vs Unsupervised - Flag fake accounts on social medias

I have this project I'm working on where I scraped users' data from social media to predict if they are bots, fake accounts or legit users based on their comments, likes, posts, public data only. I'm ...
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19 views

How to add positional embedding in an attention based model which takes custom feature embedding as input? [closed]

I am working on building an attention based deep neural network model where I fed this with a variable length of custom feature embeddings. I would like to provide positional information for my custom ...
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Constructing circular towers to show that single hidden layer feedforward neural networks can approximate any continuous function

In this intuitive explanation of why wide-enough shallow feedforward neural networks can satisfy the universal approximation theorem from any continuous function on a compact domain, the author uses, ...
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42 views

How to deal with temporal trend in ML

I am fitting a binary classifier and I observe a temporal trend in the response variable, meaning that the actual percentage of positives fluctuates with time, I can see periods where it is high and ...
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23 views

Appropriate Supervised Machine Learning Algorithm for Time series prediction

I am looking forward to the correct ML/algorithm approach for the below issue. My target here is to predict the target day of the incoming time series below for a new time series. Also below you can ...
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32 views

Some questions about supervised learning, model evaluation and preprocessing [closed]

I've been trying to employ some basic techniques of supervised learning on a dataset that I have and I have several questions about the overall procedure (i.e. data preprocessing, model evaluation etc)...
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24 views

How to split unbalanced data for supervised learning?

Suppose I have data I want to use for supervised learning, but there is a pretty bad target/class/labels imbalance. Should I: Limit the size of the training set to make sure there is a flat target/...
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17 views

Balanced target variable in KNN classification

In many places, I have seen it only mentioned that predict the label of the query point as the label with more than half of the labels of it's K nearest neighbours. However, I don't see it mentioned ...
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How to classify (supervised) a multi dimensional vector?

What kinds of machine learning tool is used to classify a vector of data which are not spatially correlated? I have a 158*158 image*15000 samples which I tried to ...
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16 views

Using customer comments to predict restaurant rating

I have a list of features of a restaurant on which a customer gives a comment (from given options). I also have the overall rating of the restaurant. I would like to use this data to build a model to ...
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51 views

KNN error: could not find function "train" [closed]

this is my KNN code: ...
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24 views

New classification in Machine Learning model with xgboost

I write a code in Rstudio with xgboost to solve a Machine Learning problem. This is my actual code: ...
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How can I assess feature importance when determining whether a missing data is MCAR or not?

I was reading some lecture notes on missing data and the author suggests the following approach to determine whether some varibale is missing completely at random (MCAR) or not: Supervised Learning ...
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45 views

What does "S" in Shannon's entropy stands for?

I see many machine learning texts using the following notation to represent Shannon's entropy in classification/supervised learning contexts: $$ H(S) = \sum_{i \in Y}p_i \log(p_i) $$ Where $p_i$ is ...
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Advantages to combining similarly-named columns for supervised ML?

Is there any benefit to combining similarly named columns either for an improvement in accuracy or for speeding up training/prediction in case of logistic regression, random forest or neural network ...
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Dealing with unbalanced training set compared with real world data

I am in charge of a fraud detection model that prevents fraudulent users from using our solution. My model is performing great but the issue I have is that the more the model becomes performant the ...
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1answer
43 views

Two-level (large category and small category) label classification problem

At present, there is an app classification task, the input is the function description of the app, and the two labels are the major category to which the app belongs and the small categories under the ...
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45 views

Supervised clustering use case?

I'm currently working in a problem, where I think a supervised clustering approach might be a good candidate, but I'm not sure and haven't really worked with such scenario before. Let me break it down:...
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51 views

How do I deal with unbalance classes in a stock market prediction problem?

I am working on a prediction model to predict whether a stock should sell, hold or buy in n days. Each day (or row in the dataset), I classify whether this should ...
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19 views

Which algorithm works well for forecasting sales prediction and the reason to choose particular algorithm?

I am working on a project 'Rossmann Sales prediction', in which I have to forecast the sales of Rossmann Stores. So it is a supervised ML problem. I applied random forest. But then in interviews ...
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How to use the eval set in catboost appropriately?

Let's say you have a dataset, and you split it into 80% training and 20% testing. Naturally, you want to find the optimal hyperparameters for your model, so with the training set, you plan to do cross ...
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70 views

How to model a arrival process with increasing features?

Suppose a website records all information related to visits including gender, device, time, etc. When a new impression happens we store it and we want to predict when this person will re-visit the ...
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43 views

Does BERT need supervised data only when fine-tuning?

I've read many articles and papers mentioning how unsupervised training is conducted while pre-training a BERT model. I would like to know if it is possible to fine-tune a BERT model in an ...
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How to solve Bellman-Hamiltonian-Jacobi in stochastic environment through machine learning?

Suppose there is a stochastic optimal control problem where the uncertainty is because of the random arrival of entities into our system which is modelled as a Poisson process with rate $\lambda(p)$. ...
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78 views

New classification in Machine Learning KNN model

This is my example of KNN model (I write it using R): ...
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23 views

How to extract features insights to change classifier decision?

I don't know if my question is specific enough but there's what I mean. Suppose we have high school grades of students who attended a Computer Science degree and whether or not they succeeded (given a ...
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36 views

How to set the priority to Machine leaning algorithms for Binary classification among Four based on accuracy and fitting

Rain Classification in Australia Under this context, sklearn classification algorithms will be used, namely: Logistic Regression Classification (Parametric) Decision Tree Classification (Non ...
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22 views

Can I improve a supervised learning model if I have the breakdown of the target variable?

I have a variable y_total, which I aim to predict using features x. Actually y_total is the ...
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Is it the case that ACF and PACF reflect full information about ARIMA model parameters (p,q)?

Let say i have a single time series of N observations. I'm wondering how informative are ACF and PACF functions of this series. As we know, they can be used to infer orders of AR and MA part of ARIMA ...
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359 views

How to fix Sagemaker's "No finished training job found associated with this estimator" error?

I ran a complete AWS SageMaker Autopilot experiment. I now want to generate batch forecasts using this model but I get the error: "No finished training job found associated with this estimator. ...
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47 views

When should I use 'rbf' and 'polynomial' kernel trick in machine learning algo?

I have a problem about hate-speech classification using support-vector machine algorithm. The task is to identify the sentence that contains 'positive' or 'negative' sentiment. Which is the best ...
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25 views

How to train a classification algorithm with multiple samples that represent the class?

Hopefully explaining this is the right way. Apologies if some of it is unclear at all. I am working with network data and want to use a supervised approach to identify whether a sample (packet) is ...
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44 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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29 views

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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48 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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16 views

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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62 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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23 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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22 views

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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1answer
105 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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19 views

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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127 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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77 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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1answer
22 views

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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1answer
64 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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1answer
22 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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9 views

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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58 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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24 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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