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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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 ...
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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 $x_i$ ...
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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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Modelling an anticommutative function with a neural network
Are there any tricks or best practices for modelling an anticommutative function with a neural network?
That is, you're performing supervised learning trying to learn $F(Q,R)$, and you know a priori ...
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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 is self-supervised learning different from feature selection or using dimensionality reduction
I am getting a bit confused in understanding self-supervised learning and how is it different from normal feature selection method. I understand that in self-supervised learning we are actually ...
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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 ...
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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 ...
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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 ...
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Oddly classifier is more accurate than regressor for solving a regression problem - what could be happening?
I am working through a simple tabular supervised machine learning problem.
I have a continuous target variable y that is normalized to the interval 0-1 to represent ...
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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 ...
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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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Supervised UMAP on multi-label data
Is training a semi or supervised dimensionality reduced space with UMAP using multi-label targets supported & known to yield meaningful results (with respect to the unsupervised embedding)?
The ...
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Lasso regression / SVM convergence CPU -> GPU
I have coded a simple supervised ML classification using 10-20K data points for 25 samples. Linear ML models run quickly for example naive Bayes, linear regression and SVM linear on a small multi-core ...
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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 ...
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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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When you plot features(pairplot) and gets many vertical plots, which non-linear regression would be useful to make sense of it?
I have plotted my N features against the output and I have multiple vertical plots and was wondering, if I should go by decision trees.
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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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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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A/B testing for channel preference
Right now my organization runs a lot of promotional campaign every month. We send email, SMS and WhatsApp to all customers for each campaign. I am running a project to identity the best channel for a ...
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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 ...
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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 ...
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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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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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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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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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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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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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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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