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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 ...

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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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Loss function become worse after randomization

I have a neural network model for regression my data. I have around 85000 data with 7500 data attributes. When i do my training, i am using mini batch gradient descent with size 25 for every mini ...
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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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How to validate if partitions of a training set have been generated by the same joint distribution?

Following Luxburg (2008) in a supervised learning problem: We do not make specific assumptions on the spaces $X$ or $Y$, but we do make an assumption on the mechanism which generates those training ...
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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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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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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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10 views

how to calculate Precision and Recall, when we used classification learner app to train our model?

I'm going to use classification learner app for prediction in networks. so i need to evaluate my method with different kind of measures like Accuracy, precision, Recall, F_measure. but when i use ...
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1answer
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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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46 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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For reinforcement learning, how to solve wrong recording issue when learning from expert?

Suppose I want to build an AI to play a game, I play it first as the expert for AI to record. But there are some issues: For adjacent frames, for example, the actions are →,→→,when recorindg , how ...
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Model selection and conclusion on prediction

I am predicting the taxi demand in NYC depending on time and location. So far, I am considering 3 models: linear regression ridge regression random Forests In order to improve linear regression I am ...
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1answer
27 views

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
9 views

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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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
144 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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What causes a high testing deviance vs. training deviance in a gradient boosting classifier?

My main goal is to classify multi-class data using supervised learning. Currently, I am looking into GradientBoostingClassifier as the estimator. I want to make sure I am selecting the model ...
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34 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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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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1answer
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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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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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34 views

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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21 views

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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35 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
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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
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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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29 views

(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 ...
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2answers
185 views

Model for predicting chance of winning in variable count of opponents

I have dataset with horse racing results including bookie odds - converted to percentage chance of winning. Data are stored in relation tables. The basic entity relation is described on image. Each ...
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What are the disadvantages of Random Forest Algorithm? [duplicate]

I am using random forecast algorithm via python sklearn library to forecast data. So far it's accuracy on my training data is good. I am using the algorithm to find the predict a decision based on ...
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29 views

Method of finding optimal parameters - Supervised learning versus reinforcement learning

How to choose the best approach to optimization problem? I have a working simulation of the problem at hand that is slow and want a quick way to determine the optimal operating parameters based on a ...
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36 views

How to add predictors to LSTM time series model? [duplicate]

I'm doing Long-Short-Term-Memory (LSTM) to forecast time series. I was wondering, could we add x-reg part to an LSTM model? Like adding an X-Reg part to and ARIMA model? Say I have a response monthly ...
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1answer
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What is the difference between model selection and hyperparameter tuning?

In the context of supervised learning, in most statistics based texts and papers, one reads about model selection. For example Hastie, Tibshirani and Friedman in ESL define it as: Model Selection: ...
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Forecasting with two rank correlated data sets

I have a machine that produces widgets. I know the more widgets the machine produces the more heat it produces. When I observe the machine I see the following temperatures: [20,21,19,25,30,40,45,47,...
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2answers
219 views

SVM: Why alpha for non support vector is zero?

In the optimization problem in SVM to compute the margin, we use Lagrange multipliers to insert the constraint: $L(w,b,\alpha)= \frac{1}{2}|w| - \sum \alpha (y_i(w*x_i+b) -1) $ Now we want to compute ...
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Cross Validation - Are folds and reps learner dependent?

If I read relevant papers I often get the advice to use 10 fold CV or repeated CV instead of a 5 fold or 3 fold CV for tuning a certain learner. The reason is that especially the 10 fold CV with ...
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3answers
150 views

What do we mean when we say that an approach is “Bayesian”? [duplicate]

I'm trying to explain to a nontechnical colleague of mine what a Bayesian approach is. I realized that despite having used Bayesian methods on more than one occasion in the past, I don't have an ...
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1answer
37 views

Is there any work done on reconfigurable convolutional neural networks?

Convolutional Neural networks are used in supervised learning meaning models are always "set in stone" after training (architecture and paramters) so this might not even be possible, but is there any ...
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Classify a specific object amongst other diverse objects

I have a device which takes one picture per day of a slab. It contains many instances of a specific object (let's call it "Object A") and a few other objects (let's call them "Others"). I want to ...
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157 views

Selection of random forest regression models based on r2_score

I'm making a regression model which predicts the concentration of air pollutant. It consists of the following features: Features Things that I have done so far : Assigned mean values to the missing ...