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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1answer
22 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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1answer
15 views

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
34 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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2answers
38 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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2answers
40 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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1answer
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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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1answer
69 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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1answer
29 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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1answer
53 views

New classification in Machine Learning KNN model

This is my example of KNN model (I write it using R): ...
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1answer
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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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1answer
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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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1answer
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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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1answer
110 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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1answer
34 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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1answer
24 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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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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1answer
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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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1answer
42 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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15 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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1answer
40 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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1answer
21 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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1answer
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
43 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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1answer
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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1answer
52 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
55 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
19 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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1answer
46 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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22 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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19 views

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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1answer
17 views

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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1answer
29 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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13 views

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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26 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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1answer
31 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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10 views

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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31 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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2answers
45 views

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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1answer
46 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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1answer
162 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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