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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2answers
32 views

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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1answer
32 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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1answer
17 views

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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14 views

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
19 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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1answer
20 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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19 views

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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1answer
37 views

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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1answer
26 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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1answer
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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
30 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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1answer
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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
59 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
283 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
90 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
37 views

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
117 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
54 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
44 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
79 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
29 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
55 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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42 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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20 views

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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1answer
35 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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1answer
54 views

what information can we obtain from t-SNE?

I see that t-SNE can help us reduce dimensions and visualize the data. But what information are we gaining from this visualization? As we know that the new axis don't have a meaning in our context. ...
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43 views

Do I need to use learning algorithms for this type of data?

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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14 views

Predicting next day value

I am new to Data Science and I am trying to solve this problem. It is a problem of supervised learning. I have a dataset that for every day of a time interval, for every defined geographic point, has ...
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103 views

Data is hard to learn as a whole, easier to learn after splitting logically

I have a 3D spaceship duel simulation (without the source code). I need to build a model that will learn the simulation's behavior. I can run the simulation as many times as I want, randomly feeding ...
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16 views

Classification of text articles found in different PDFs

I am trying to figure out the approach to connect articles present in different pages or PDFs. The historical data that I have are: Text Articles (broken down in headlines, body text, body headline ...
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1answer
20 views

How to use id's in binary classification problem

I would like to predict for a given user (on a website) if he/she logs out from the website within ten minutes. In terms of data, I have a user ID and timestamp of the latest post on the website. ...
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21 views

Example of Reverse KL Divergence in Supervised Learning

It is of common knowledge that Supervised Learning uses forward KL divergence. However, I would like to use Reverse KL Divergence and am looking for examples of similar usage in literature. Most ...
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1answer
23 views

Exploratory Data Analysis on dataset divided by winners and losers

I have a dataset where I have features from winning tennis players and the other half are from a losing tennis players: ...
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67 views

How to detect payment's fraud using Machine Learning?

While browsing Machine Learning problems, I came across this question: "Given a month’s worth of login data from Netflix such as account_id, device_id, and metadata concerning payments, how would ...
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1answer
65 views

How to set different weights for different training samples?

Suppose that your supervised learning training set is made out of 3 different datasets, merged into a big one. Because of the way each of those was labeled before merging, you might be suspicious that ...
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2answers
45 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The dataset looks ...
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What methods exist to compare two corpora for suitability of supervised model use?

Whereas so many Kaggle contests I have done have a test set which is withheld from the entire single dataset, often times I find myself asking: if I train a supervised model one this one corpus, how ...
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1answer
159 views

Which supervised machine learning algorithms assume normally distributed feature variables?

I want to understand the assumptions made by supervised machine learning models. I've heard it said many times that 'you need to make sure your feature variables are normally distributed for your ML ...
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1answer
49 views

How to train a machine learning algorithm with multiple labels

I have the following challenge and I very much hope that there is a solution to it. I also suspect that there is a simple approach to it. I just don't see it at the moment. Any help or advice is ...
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24 views

What ML model should I use for matching 3 datasets on a selection of common columns?

I am looking for suggestions on which model might be appropriate for my case. I have 3 tables that I need to match. Column names don't match but I can modify them. Fields ... do match to some ...
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19 views

Marrying classic features with text/chat data

This is an approach to a problem / advice seeking question: I just wanted to get more experienced opinions on how best to combine (in a categorization context) user text/chat entry data and the ...
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1answer
28 views

Is using unsupervised learning to setup supervised classification reasonable?

I've got a biological dataset describing genes. The overall idea is that there are thousands of these genes to sort through, so if ML can rank them I can then know which should be going into the lab ...
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1answer
114 views

Suspiciously low False Positive rate with Naive Bayes Classifier?

I am performing phishing URL classification, and I am comparing several ML classifiers on a balanced 2-class data-set (legitimate URL, phishy URL). The ensemble and boosting classifiers such as ...

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