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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Word stemming effect on dictionary-based Sentiment analysis

I am currently building a Farsi dictionary-based sentiment analysis model, based on thousands of Farsi tweets. Our team's approach has been as follows: ...
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A conceptual question about training loss

If i have two datasets of the same classification task (e.g. dataset 1 with 1000 samples and dataset 2 with 10k samples) and i train 2 identical models on these datasets with the same hyperparameter, ...
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SkLearn DecisionTree doesn't include numerical variables after one hot encoding pipeline

I'm trying to fit a dataframe with SkLearn DecisionTree with the following code. But I get a error Length of feature_names, 9 does not match number of features, 8. ...
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does constant input add information in training a neural network?

I am working on an image denoising task. My noise pattern is generated from a 3rd degree polynomial function of images. I have multiple sets of 4 images (called tables) to generate different noise ...
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Using k-means to create labels for supervised learning

I want to know if the following is a valid approach to create labels, if I have measurements under some conditions, and the conditions are similar but never exactly the same. This doesn't correspond ...
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Laben Encoding for Target Classes: Any Integer or Consecutive Integers from Zero?

I'm handling an very conventional supervised classification task with three (mutually exclusive) target categories (not ordinal ones): ...
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High Performance Classification or Similarity Algorithim for Mixed Data Types?

I have a database holding 10-ish features that describe different breeds of dogs. They are mostly categorical features, but some provide ranges for values. Here's a demo representation of the database,...
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Can clustering results based on probability be used for supervised learning?

I'm a beginner and I have a question. Can clustering results based on probability be used for supervised learning? Manufacturing data with 80000 rows. It is not labeled, but there is information that ...
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Proper iteration over time series data for LSTM neural network

I’m using the supervised learning method with an LSTM network to predict forex prices. To achieve this I’m using deeplearning4j library but I doubt several points of my implementation. I turned off ...
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Model description classification supervised learning

I have a question, is it correct to say if we consider our problem as supervised learning for a classification task: In input, we have real numbers and in output, we have a category. The model we are ...
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Document understanding - sentence length prediction

the subject is taken almost verbatim from this paper https://arxiv.org/pdf/2108.02923.pdf. One of the tasks , is to be able to tell, in a document, if 2 words are part of the same phrase. For e.g. if ...
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Functional form for machine learning models

I am new to the field of machine learning and I have a question. Is there a way to print the function of any machine learning model, just like Y=mX + C (equation for straight line). For eg. support ...
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What are the benefits of combining semi-supervised and supervised learning methods?

I've been looking into semi-supervised learning more, specifically label propagation and label spreading. When reading through tutorials and some papers I've seen it mentioned that often times the ...
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Why Should There Be Multiple Columns in Train Labels for One Model?

Going through the notebook on well known kaggle competition of favorita sales forecasting. One puzzle is, after the data is split for train and testing, it seems ...
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Text segment identification without clear boundaries

I am considering the problem of creating a supervised machine learning algorithm able to identify segments of text that are of interest, but where the segments will not, in general, coincide with a ...
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Can I create a new target value based on the average target value of same data points for regression?

I am trying to predict profit of retail stores. The orginal dataframe looks like this: Store No feature A feature B year profit A 1 2 2016 20000 A 1 2 2017 40000 B 4 3 2017 50000 B 4 3 2018 40000 ...
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Is there any works in the direction of dimensionally reducing the size of DNNs?

I am talking about a scenario where you first train a "huge" Neural Network and then try to scale it down without sacrificing much of the accuracy. I am not talking about quantization of ...
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Training, Validation, and Testing Data in Supervised Learning

I've come up with some simple definitions for training, testing and validation data in supervised learning. Can anyone verify/improve upon my answers? Training Data - Used by the model to learn ...
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ROC_AUC score is higher before tuning n _neighbors for KNN

This is for multiclass classification. Before tuning the n_neighbors for KNN, these were the results: ...
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Self-supervised learning. Why is it useful?

Self-supervised learning implies that algorithms are trained to predict missing pieces. Say, I take a sentence "I like cats", remove the word "cats" and train an algorithm to ...
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Classification for two dimensional data

I have time series like 500 data points of $(x,y)$ pairs, where $x$ = time in seconds and $y$ = signals. Each of these candidates/time series has an additional label, which tells about the nature of ...
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Distinguishing text with opposite meanings in SVM (False Information Detection)

I am currently working on a Binary Text Classification Model (False Information Detection) using Support Vector Machine and used TF-IDF as text vectorizer in Python. I have already tried training the ...
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Multi-Label Text Topic Classification

I have a huge dataset of messages/comments classified with topics. The dataset consists of 1kk records and have a total of 90 topics, like this: ...
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is using feature selection(supervised) methods after running kmeans and taking the 'cluster' variable(0,1,2 for eg.) as the labeled data correct?

Feature selection in a gist from what i understand is reducing the variables but retaining the labels as much as possible, from that pov this seems correct but i haven't found anything on this. Any ...
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Is Yoloact working on different images?

is it possible to train Yolact on one dataset containing various image sizes? I've used different cameras so resolution and size are varying.
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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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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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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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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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2 votes
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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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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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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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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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KNN error: could not find function "train" [closed]

this is my KNN code: ...
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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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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 working on 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 less ...
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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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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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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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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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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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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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New classification in Machine Learning KNN model

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