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Questions tagged [supervised-learning]

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

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Using a supervised learning to compare two conditions

I've got to analyze data about two signals x and y using machine learning but am stuck with how to proceed. Conditions are: 1) signal x and y are known to be linked to one another, but no parametric ...
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Modeling multiple outputs - one model or several

Recently at work I enter an interesting discussion that I thought could continue here and receive your output. I'm trying to model some data that have as an output a categorical variable (let's say X)...
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Linear regression feature selection equivalent for a classification problem?

I have the following: Label (y): a boolean flag indicating something is good or bad Features (X): lower-level features that are believed to be correlated with the boolean flag. Some of them are ...
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1answer
25 views

Kernelize Linear Regression

We can kernelize Ridge regression as shown in these notes: https://www.ics.uci.edu/~welling/classnotes/papers_class/Kernel-Ridge.pdf. However would it be possible to find a vector $\boldsymbol\alpha$...
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How do the kitchen sink approach used to extract Algorithm's feature?

Hi while reading the article of Predicting Unroll Factors Using Supervised Classification of Saman Amarasinghe and al. they mentioned that they used kitchen sink approach for features extraction. ...
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Impact of C on geometric margin in linear SVM

Will the geometric margin always decrease if we increase $C$ in a linear SVM? When data is linearly separable, that makes sense but I can't really see it when we have nonlinearly separable data.
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Best strategy to maximize the prediction accuracy when p >> n

I am solving the following classification problem: thousands of features, but only 40 samples (i.e. p >> n) classes are balanced it is not possible to get more data the only thing I am interested in ...
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Conditions for Adaboost to perform well

Under which conditions does the AdaBoost algorithm yield good results even on weak learners (i.e. slightly better than random classifiers)?
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Parallel Bagging in supervised learning

How can we parallelize Bootstrap aggregation, a.k.a Bagging, i.e. train all classifiers at once?
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1answer
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Advantages of dual formulation

Why do we solve the dual form of the SVM in practice to obtain a classifier instead of the primal?
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Must all supervised algorithms have (complexity) parameters?

I have noticed that all commonly used supervised algorithms (decision tree, logistic regression, random forest, ...) have some (hyper)parameters to tune (otherwise the model may underfit or overfit ...
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Machine learning to detect wear on a machine axis

I have a machine that moves with one axis in the same direction (basic position A to end position B). While driving, the torque is measured and recorded every 10 milliseconds. This looks something ...
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1answer
23 views

AUC ROC when one class consists of smaller subclasses

This question is different from Binary classification when one class consists of multiple subclasses I have two classes that I want to distinguish by a supervised learning classifier such as a random ...
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1answer
36 views

Training error not decreasing on the training set

I cannot make my neural network - MLP with 1 hidden layer fit the training data perfectly. Here is the data: ...
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With test accuracy being equal, is it better to have lower training accuracy?

Suppose we train two models on a training set, and then test them both on the training set itself, and on a test set. We have some accuracy metric we're using to evaluate them. Both models score ...
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1answer
54 views

Would a Logistic Regression Machine Learning Model Work Here?

I am in 10th grade and I am looking to use a machine learning model on patient data to find a correlation between the time of week and patient adherence. I have separated the week into 21 time slots, ...
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Why is Backpropogation used instead of Rosenblatt's learning Algo or gradient descent to train MLP's?

In roesnblatt's learning algo and gradient descent the output is calculated for each input and based on the error b/w the outputs calculated and desired outputs the weights are updated. Why is ...
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Applying Label From Supervised Learning to Unlabeled Data- Text Classification

I am wondering if anyone has code to following: 1) Apply labels from a previous text classification dataset like this type of data (https://colab.research.google.com/drive/...
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Why are decision trees considered supervised learning?

It seems to work similar to clustering algorithms, where data does not have to be labeled, and the algorithm creates it's own labels/groups based on feature similarities...
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K-Nearest-Neighbor classification with only distance/similarity matrices, is it possible?

I want to classify histograms/distributions using K-Nearest-Neighbor. I can measure distances/dissimilarities between the distributions (using euclidean distance, kullback-leibler divergence...), thus ...
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inferring most important features

Given a set of $n$ instances. For each instance I have a feature vector consisting of $m$ (numerical) features ($x_1$, $x_2$,...,$x_m$), n>>m. Moreover, for each instance I have a numerical score $y$ (...
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External validation of clustering requires labels, but why cluster at all if you have labels?

There are two types of validation in clustering, using: Internal indexes: Used to measure the goodness of a clustering structure without respect to external information (e.g., sum of squared errors)...
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leave-one-out cross validation on images that have a discrete labels

How should I do leave-one-out cross validation on images that have a discrete labels (either Python or R)? Most of the examples I see are quite different (they are not images).
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Class of Interest in supervised learning

The positive class in a binary classification problem, is usually the class of interest (e.g: fraud, spam, cancer). Machine learning algorithms try to construct a classifier that can separate these ...
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1answer
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What happens if I train a model on a data set that includes a duplicated feature?

The Question Suppose I train a predictive model on a set of features $x_1, \dots, x_n$, but for some $i \neq j$ we have $x_i = x_j$ for every data point in the training set; i.e. one of these ...
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1answer
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Comparing Supervised ML algorithms in R on same data set

I've recently embarked on my data science journey, and I've therefore also started a data science course. In this course, we've received an assignment asking us to model a data set using different ...
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1answer
23 views

Defining target for Supervised learning classification

I would like to know if there is a way to predict an outcome (successful/failed or $1/0$) with and without a binary variable and compare their predict probability. I have several variables. However, ...
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1answer
53 views

More features, less F-Score

Is there any rule about relationship between number of features and performance of the model? Recently, I did an experiment on 3 sets of features (all extracted from a same dataset). The strange point ...
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Compare accuracy between tools using k-fold cross validation, each tool is tested with different k values

I'm working on a new way to do the classification in a supervised way and I want to compare its accuracy to some related works. These works are using the same data set and they are testing their ...
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1answer
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Some examples correlated - best strategy to split?

I have a data set of companies having different feature variables like number of employees, sector, revenue or location. And I also have a target variable (energy consumption) I want to predict by ...
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1answer
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Supervised learning with error-range in labels?

I am working in a problem where labels have an error range (we know the range). For instance, a label can be expressed as $y_i \pm e_i$ with $e_i$ is the error range for the label of the instance $i^{...
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43 views

Linear Discriminant Analysis as Dimensionality Reduction very sensitive to Training Set size

I'm working with supervised classification of object-based satellite imageryand currently investigate different dimensionality reduction methods on their suitability to this application. As part of my ...
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2answers
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How to define a time series classification problem?

I have 3 sets of time series data generated from sensors, I believe they have some correlation themselves. Certain "modes" of the system can be defined from the patterns from these signals. The signal ...
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1answer
391 views

Knn Decision boundary

I am new to machine learning and trying to draw decision boundary for k nearest neighbor where k=3. I know that the decision boundary for k=1 would be the perpendicular bisector between two different ...
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0answers
56 views

Classification model on a highly unbalanced dataset [duplicate]

I’m dealing with a highly unbalanced dataset where 20% of data belongs to class A and 80% belongs to class B. It’s very hard for us to produce synthetic class A data. Just wondering if the below ...
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1answer
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Is supervised learning a subset of reinforcement learning?

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labelled data (as opposed to other information in ...
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1answer
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Regression with multidimensional output variable Y

Say we have an $N \times q$ matrix $Y$ with $N>q$. Also, we have an $N \times p$ data matrix $X$. We are interested in a model of $Y = X \times W + \epsilon$, where $W$ is a $p \times q$ matrix ...
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2answers
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Training error in KNN classifier when K=1

I got this question in a quiz, it asked what will be the training error for a KNN classifier when K=1. What does training mean for a KNN classifier? My understanding about the KNN classifier was that ...
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64 views

what's the difference between semi-supervised learning and partially supervised learning? [closed]

Isn't every semi-supervised problem also a partially supervised learning problem and vice versa?
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How can I tell a model reached the optimal parameters?

Aside from stacking more models, If I want to know if I have arrived the best possible single model(the best parameter), is there anything/process I can tell? Assume I made n-degree of polynomial ...
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What exactly is semisupervised learning?

I have come across two descriptions of what semisupervised learning is, where one would have a small set $\mathcal{L}$ of labeled data and a larger set $\mathcal{U}$ of unlabeled data. The first ...
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confusion about multiclass linear classifier

I notice that there is a bit of confusion in multiclass linear classifier notation in at least 2 points: from Bishop's book and for example these slides they call the One-versus-the-rest approach (...
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64 views

LSTM frame time series to a supervised learning problem

I just begun to play around with LSTM. Therefore I read the guide from this site Multivariate Time Series Forecasting with LSTMs in Keras The task is to predict the air pollution. I understand the ...
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1answer
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Improving supervised learning for question text comprehension when there is no obvious answers

I'm trying to determine how to answer question from text with supervised learning. This used to work quite well when every questions had answers. Here is the head of dataset we used with the sentence ...
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1answer
60 views

clustering VS supervised classification, in the case of very small database

I'm trying to classify/cluster subjects according to 4 features in two classes: healthy and sick. Two things to know: I know the labels/classes of each subject + I only have 40 subjects (in total: ...
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1answer
152 views

unsupervised classification VS supervised classification when data labels are known

Can someone give me some scenario where it's better to use clustering (unsupervised classification) than supervised classification such as SVM ? I mean in a case where you know the data labels/classes....
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Semisupervised and Multiclass Classification

I have a dataset that includes around 400 instances (400 users' instances) with 10 features. As follows: ...
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(Re)-Train on a small dataset and new incoming data

I would like to train a classifier (doesn't matter which learning algorithm) on a small set of training data. As soon as the system predicts new samples, it should collect them, add the samples to the ...
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3answers
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What *is* an Artificial Neural Network?

As we delve into Neural Networks literature, we get to identify other methods with neuromorphic topologies ("Neural-Network"-like architectures). And I'm not talking about the Universal Approximation ...