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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Measures of efficacy for one classification models on the same data set with different numbers of classes?

I am currently doing a university project in supervised learning. The variable to be predicted varies across the integers [0,100] and my supervisor suggested to split this range into different classes ...
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How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data?

How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data? So far, I've only been training with labeled data on fraud-...
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How does ROC work with SVM?

Could someone please explain how ROC works with SVM? Specifically i'm using RocCurveDisplay.from_predictions(y_test, y_pred, ax=ax[1]) which works fine. Since the ...
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unsupervised clustering followed by modeling each cluster to create a mixed model

I am curious if this is an advisable approach. I am not applying this approach and am only interested in the theory of it. let's say you have some set of features X and target Y. X can account for ...
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How to deal with a dataset in which categorical features have one value for specific class?

I have a multiclass problem and for the class equal to 2 in the target I have some categorical columns with just one value. For instance, is like for the observatuons with the target equal to 2, the ...
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How should I use ML to extract the entries from a dictionary?

A question about methodology I know that methodology questions are not welcome on the Stack Overflow site, but I don't know if they are acceptable here. If they are not, I apologize. Description of ...
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Why does data science see class imbalance as a problem for supervised learning when statistics does not?

Why does data science see class imbalance as a problem in supervised learning when statistics says it is not? Data science seems to seem class imbalance as problematic and needing special techniques ...
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Improving performance of anomaly detection using dataset?

I am leveraging an isolation forest model from the scikit-learn library for anomaly detection in a time series dataset where each point in the dataset is a data frame. However, I possess additional ...
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Counting Number of Holes in an Image of Cheese

I've been assigned a project that involves writing a script to detect the number of holes in an image of cheese. My background in AI is quite limited, so I was wondering if anyone could give me a good ...
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Looking for a way to train a model to learn parameters for clustering

I have 5000 docs, each is a review. For each review, i'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
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How to build a regression model with same targets for different observations?

I'm working on a project involving around 90 3D-printed cubic samples with different structures. After conducting a compression test, I obtained stress-strain curves with 700 data points for each ...
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Machine learning can be broken down into supervised, unsupervised, and reinforcement learning. Is there anything else?

Is it even logically possible to have a type of machine learning that doesn't fall into those three paradigms? Supervised: a dataset of inputs and outputs are fed to an algorithm which learns a ...
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how to build model using two input dataset in which there is no common column to merge or combine

I want to create model for truck company in which trucks delivers the car for customers.i have two data sets. one is customer details like how many cars they want from particular area or terminal and ...
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Predicting a finite multi-set from a finite set given vector-valued data

I am unsure in how to approach this problem, which is a supervised learning task, and was looking for ideas on how to tackle it since I am kinda confused. Abstract description: I have a source set $S:=...
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Automating the task of figuring out if a task is classification or regression

When manually identifying if a given dataset and dependent variable are suitable for classification or regression I look at the type of variable (continuous or discrete) in which the name and values ...
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Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'numpy.ndarray'>"})

...
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How does supervised fine-tuning work in InstructGPT?

See Figure 2 from the InstructGPT paper: I want to know how Step 1 works. Here is one possible algorithm. Pass the prompt through the model, and compute the negative log of the probability of the ...
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Is SVM is a good choice for large dataset?

With my limited knowledge of SVM, I am following a tutorial on YouTube to create an End-to-End multi-class ML model . There the person is using SVM on a dataset with 9 images dataset, but the dataset ...
abhi singh's user avatar
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Why does training the exact same model sometimes work but other times not work?

I have a simple toy model that I'm using to learn from (identity function). The dataset is every increment of 0.01 from [0, 1], for both $x$ and $y$. So if $x_i$ is 0.01, $y_i$ is also 0.01. If $x_i$ ...
mathbike's user avatar
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How would you build a Supervised Learning model that predicts the next number in the sequence?

I'm trying to build a toy supervised learning model in order to understand it better but I'm making an error somewhere. I know this model doesn't make practical sense, but it should be possible to do. ...
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How to Use Graph Learning Libraries to Predict Edges on a Graph where Each Node Has an Embedding?

An undirectional graph $\mathcal{G}$ has the set of nodes $\mathcal{N}$ where each node has an associated unique embedding of $512$ dimensions. Note that the embeddings themselves are fixed, and not ...
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Supervised time series anomaly detection

I have time series data. Dataset contains around 600.000 metrics. Each metric published daily and has three values, let's say 'count', 'number of something', 'length of something'. Looks this way: <...
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Seeking Feedback on Methodology for Implementing Supervised Classification ML Algorithm for Customer Satisfaction Prediction

I'm currently designing a methodology for implementing a supervised classification ML algorithm and seeking guidance to ensure I'm heading in the right direction. The problem I'm addressing involves ...
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randomness in lightgbm model training

What are the parameters that add randomness to the training of a lightgbm model? (for a large dataset) I have tried setting all parameters as default and letting bin_construct_sample_cnt be greater ...
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how each tree in random forest structured/built?

I'm new to machine learning and I want to use random forest for the problem I have. What I have done so far is I did the 80/20 split of the original data set. I need to understand what will happen ...
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How can I manually annotate data in XML?

I am involved in a project where we need a solution for manually annotating the contents of patient medical records stored in an XML format. We need a tool to show the contents of the fields of the ...
Thomas Arildsen's user avatar
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Combine machine learning feature selection with time series

I have basic knowledge in time series prediction and supervised/unsupervised machine learning algorithms (clustering, classification, decision tree, etc.) I am now given a task to predict a bunch of ...
Alex's user avatar
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Topic classification on text data with no/few labels

I would like to achieve a classification of a text input into predefined categories. From what I have understand unsupervised approach are unfeasible if my target label is something very rare in ...
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How to calculate accuracy of a logistic regression?

A logistic regression involves a linear combination of features to predict the log-odds of a binary, yes/no-style event. That log-odds can then be transformed to a probability. If $\hat L_i$ is the ...
Dave's user avatar
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How to classify text and predict if it belongs to the group or not?

I am basically Python Postgres programmer and new to datas science and its tools. I have around 78 million records which contains information like this: CostCenter Description 110000032 Hiring of ...
Oneflydown's user avatar
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Problem with data representing classes that weren't present during supervised training

During prediction phase, fully trained supervised models may have to deal with data representing new classes, that weren't part of the training and test sets. A real world example for this issue is ...
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Prediction of a partial input

In the context of supervised machine learning, is there a way to make a prediction of a partial input (i.e., some features are unknown) in general? If not, are there models that support this feature? ...
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How to use labels to fit several thresholds in a simple decision rule?

I have a binary labelled dataset with numeric features. I want to create a "business rule" of the type y = x1 > t1 and x2 > t2 and x3 > t3. ...
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What Would Be a Good Measure of Feature Importance in Regression?

Doing simple supervised regression where the label is a floating point number (guaranteed positive) and the features are a mix of continuous floating point values and some categorical features. What ...
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What is and why use blocked cross-validation?

I was reading about cross validation equivalents for time series data and found a variation called blocked cross validation. On the page I was reading it says the following: "However, this may ...
Pedro Silva's user avatar
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Advice on How to Train Neural Networks

I am relatively new to neural networks and AI, and I have a question regarding the training method in such networks. In particular spiking neural networks (SNNs) are the type we are working with. I am ...
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Deep learning through backpropagation: not learning

I am starting with deep learning and decided to code a backpropagation algorithm on Python 3. I have followed many tutorials and have taken as example many programs that work. Yet, for some reason, my ...
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Does it help to have similar values for features in train and test data to make accurate predictions?

I am quite new to some concepts of machine learning and having hard time understanding the following. Suppose I have a supervised classifier (random forest) trained with a dataset with several ...
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Different training score but same test score when using pipeline

I have a problem that produce different training score when using pipeline and manual. MANUAL : ...
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1 answer
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Can we train of a binary classifier with "A" to classify "a"?

I have a maybe naive question about the appropriateness of using binary classifications. This is a hypothetical example, so forgive me if it is too coarse. Let's say I want to train a support vector ...
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class label is less than 1 percent in classification problem

I am working on a ML problem where one class label is very less than even 1 percent. i.e 0.0002% I have tried undersampling, oversampling, SMOTE but the results are not satisfactory on the model. I ...
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I am struggling to understand the point of supervised ML models in real world scenarios

Sorry for maybe a stupid question, but I can't seem to find any explanation of it online. If supervised machine learning only works on labeled datasets - you can't use it to predict a value of ...
Ana's user avatar
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1 answer
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Labelling large amounts of audio data in automatic or semi-automatic way

I am working on a project, where I have to label the audio datasets which has thousands of data, each audio data is for one second. I have to label where it is in idle or event happening or noise. I ...
saranyaa suresh's user avatar
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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. ...
esokumamon's user avatar
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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,...
CyberBully2003's user avatar
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1 answer
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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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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 ...
lamyvista's user avatar
2 votes
1 answer
453 views

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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