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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20 views

What methods can I use for classification problems?

I am doing a walk classification problem in biometrics with closed identification set. I have 2k+ features in each walk. When I compare each probe with the gallery set I get the best results with LDA ...
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Relation between number of landmark points and learning accuracy

Is there any relation between number of landmark points on an image and the accuracy of learning these landmarks? For example for detecting nose tip and eye corners, can we say that adding some labels ...
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How to do encode this target vector containing strings

Consider a target vector like ...
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11 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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23 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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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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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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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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9 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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23 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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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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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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61 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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Semantic segmentation in high-resolution images with high variance - cannot avoid underfitting

I am working on a dataset of 2K images for a semantic segmentation problem. I want to detect and localize small objects, with the smallest mask to be 5x5 pixels. The images include 5 different ...
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Convert time series data to supervised learning problem

I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each. I am trying to frame this time series problem as a ...
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regarding the inclusion of missclassified data in final training

I find it best to ask my question in terms of cross-validation. Here it goes: Suppose a binary classification problem, for which cross-validation has been applied for a certain learning algorithm. Let'...
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21 views

Definition of weak and strong learners

What is the exact definition, or at least the main idea, of weak and strong learners? For instance, these terms are used in propositions such as "Boosting is based on weak learners". ...
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List of major ML algorithms with code

Can anyone recommend me any GitHub code repo/s or textbook/s that walks through all the major ML algorithms in great detail with code to get hands-on with? I have a data engineering background and ...
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Understanding Learning Curves

I would like to clarify my understanding of learning curves with two example plots below. I am experimenting with small data sets here between 500 and 1500 samples to clarify my understanding. My ...
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37 views

Self Organizing Map (SOM)

How do you use SOM as a supervised learning technique? Which approach can be added to SOM to turn it to supervised learning?
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Machine learning and time-based data

I want to predict conversion rates for an eCommerce store. I have data from Google Analytics with features like averageSessionDuration, bounceRate, numberOfVisitorsBySource etc. and the corresponding ...
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Which supervised ML model to use for exam/grade prediction?

So I plan on making a mobile app that will let students predict their final grades based on their mock exam results. I can train my model with previous years results. X: 5 mock results Y: Final grade ...
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Multiple input single output supervised learning ANN problem

I have a dataset of 120 tuples giving a singular output. I want to implement ANN in estimating the input which is affecting the output most. A case of optimising the input to maximise the output. ANN ...
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1answer
22 views

Standarizing the Label to postive intergers in Machine Learning Classification task, why is it recommended?

When doing a classification task suppose we have 3 targets with notation -2, -1, 0. I read somewhere it is a good practice to Standardize the labels to positive integers.. In this case suppose we ...
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Should I normalize the dependent variable in a penalized linear regression model?

When I compute penalized regression on the data without normalizing using the glmnet package in R, the lambda values and RMSE generated in lasso, ridge, and elastic net are unreasonably large. The ...
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33 views

Using GANs to generate synthetic tabular data to improve supervised learning

One topic I see some people trying is using GANs to generate synthetic tabular data for supervised learning. Also as a way to oversample the minority class in a binary classification. For me creating ...
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How to ouput buckets of probabilities?

I am dealing with an unbalanced binary classification problem. The problem is so unbalanced (2:98) and hard to predict that I am interested in probability of the positive outcome instead of trying to ...
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How to handle One Hot Encoded columns with changing categories in supervised ML Problem?

Scenario: I have the following game data about participants, game and the winner in the following format: ...
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27 views

Classification Model and Their Accuracy [closed]

I am trying to work with Classification model. I am planning to train and test my model with a large dataset (Training with 80% and testing with 20% volume - no under/oversampling). What I understand ...
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SGD converges at exactly the same val accuracy for differnet NN model architecture

Context: Supervised learning binary classification task with input vector of length 136. Using MLP with Dense layers with variations in number of layers, neurons, learning rate, l1, l2 regularization, ...
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How is a policy expressed? [closed]

In my work in behavioural cloning, I have been asked 'how is your policy expressed?' and I didn't know the answer to this. I was trying to create apply a behavioural cloning algorithm from the context ...
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Matching financial reconciliation data / matching multiple rows based on column values

I'm working with financial reconciliation data and the ask is to train the algorithm to match transactions (that are otherwise manually matched if the existing application didn't because not all the ...
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1answer
43 views

difference between supervised learning and imitation learning

I find these two concepts confusing because I feel that imitation learning is just a 'subset' of supervised learning. But after thinking hard enough, I could not think of any difference and feel that ...
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Machine learning for circular sequences

My data are sequences of real numbers $a_0,a_1,...,a_{n-1}$. The length of a sequence is fixed and equals $n$. Each sequence is mapped to a real number $y$ and I want to predict $y$ given the sequence....
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Why kmeans cluster breakup is like this [closed]

I have a galaxy spectrum data set (total 22000). Similar to an electronic wave data, two dimensional (Flux vs Wavelength). A typical set of wavelength plot looks like below Now I am doing kmeans on ...
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How to approach the dataset with a continuous and discrete label?

Let's say you're predicting the amount of money to bet in a poker game. Based on the game situation, you might decide to fold. In that case, the amount of money to bet is zero. If you decide to call ...
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How to model for choosing ‘safest’ outcome within range?

Let’s say you have a dataset of delivery service staff’s past performance: deliver person id, ...
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1answer
94 views

How to improve results from a Naive Bayes algorithm?

I am having some difficulties in improving results from running a Naive Bayes algorithm. My dataset consists of 39 columns (some categorical, some numerical). However I only considered the main ...
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3answers
294 views

Train/Test size and bias

I'm running a classifier (logistic regression). The information on my dataset are the following: dataset size= 279 observations (80/20 rule) ...
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1answer
91 views

How to include categorical fields to enhance a text classification

I would have a question on how to add more categorical fields in a classification problem. My dataset had initially 4 fields: ...
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2answers
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Why does Transfer Learning works better on smaller datasets than on larger ones?

This question is not about the utility of Tranfer Learning compared with regular supervised learning. 1. Context I'm studying Health-Monitoring techniques, and I practice on the C-MAPSS dataset. The ...
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2answers
242 views

Dose finding slope/intercept using the formula of m,b gives best fit line always In linear regression?

In liner regression We have to fit different lines and chose one with minimum error so What is the motive of having a formula for m,b that can give slope and intercept value in the regression line ,...
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1answer
73 views

Creating an “unclassified” class in Random Forest

I am trying to classify satellite based images by creating a region of interest and then classifying according to it. I am using a Jupyter notebook using python to do that. I used a Random forest ...
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1answer
50 views

High probabilities of success for wrong predictions

I'm studying the behavior of machine failures in a production scenario. For this, I generated random data to form my imbalanced training set, consisting of categorical data, which indicate whether or ...
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1answer
84 views

How to combine human-labelled data with user behavior data?

I am working on a supervised learning problem for a web-search task, where I have access to a relatively small set of human-labeled examples and lots of user-behavior data. Now, user behavior data is ...
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1answer
31 views

Is it better to have one model with more categories or less with two for multi-label classification?

For classifying text into three classes question, complain and complements where each sample can have multi-labels (question and complains, question and complements): is it better to have one model ...
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1answer
57 views

Sampling in Text Classification: can the results be considered 'reliable'?

I am testing different models (SVM, Logistic Regression, Naive Bayes, Random Forest) for predicting the class of a spam email. My target is a binary variable. I am analysing only text, no other fields....
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66 views

Problem of continuous training - Supervised learning

I am sure this is a most common problem, but would like to know by experts on how to tackle it. Note that, I mostly deal with textual data (NLP problems). When a supervised learning model is created, ...
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If a single element is missing more than 50% of its feature values, should you just remove it?

I have a simple supervised machine learning problem. My training matrix is MxN, where M is the number of records and N is the number of features. I have 600,000 complete patient records and 300,000 ...
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38 views

In supervised learning, are more data entries always better?

I am doing a supervised learning problem and have 600,000 rows of data. I divided it into a training and test set and achieved a high accuracy that I was happy with. However, I had thrown away 300,000 ...

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