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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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Linking generative, discriminative models to supervised and unsupervised learning

Definitions that I am considering: A generative model learns p(x,y) whereas a discriminative model learns p(y|x=x). I would like to verify if my understanding is correct by sharing the following ...
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Determining an appropriate cost function given the type of problem and a hypothesis function

I'm studying up on machine learning basics and the standard high-level approach in supervised ML is to define a hypothesis function that maps inputs to outputs. Then define a "cost function" that ...
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Top principal components versus most significant random forest variables

I was working on making a supervised learning model starting with a database of about 100 features and 1000 data entries. My goal is to predict a certain target variable. I tried three different ...
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Doubt in Prof Abu Mostafa's lectures [closed]

In Prof Yasser Mostafa's Lecture no. 4, he mentions Eout(g)~0 (slide no. 20/22) and he mentions Eout as error. However in lecture 2 (slide no.11/17), he replaces μ (probability) with Eout. I am ...
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fitting after training and validation

There are a lot written in StackExchange about train-validation-test split of data set. I am confuse with the following. Assume, I trained model using train set. Then I choose model using validation ...
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What is the trade off between having a larger validation set versus a smaller one?

Suppose I am comparing several models, e,g, $\{ax\}$, $\{ax+b\}$ and $\{ax^2 + bx + c\}$, $\{ax^3\}$ on data set $\mathcal{D} = \{x_i,y_i\}_{i = 1}^N$ I partition $\mathcal{D}$ into training set ($N-...
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Differentiate Semi-supervised vs Transductive Learning?

Can someone explain the difference between transductive learning and semi-supervised learning? Or is semi-supervised learning a type of transductive learning? Transductive learning is when we do not ...
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What makes a Random Forest random besides bootstrapping and random sampling of features?

After reading about random forests in the original paper and in textbooks I was under the impression that what makes the model random is bootstrapping - training each tree on a different random subset ...
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Multiclass classification- dealing with clusters within classes?

I'm currently dealing with a problem where I'm trying to predict how much a value x will change over time given input variables and am bucketing this change into separate classes (ie -100 to -50%, -50%...
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Problem about tuning hyper-parametres

I have tried GridSearchCV and BayesSearchCV for tuning my lightGBM algorithm (for binary classification). I have used 10 iterations and I have indicated scoring ="roc_auc" In the first iteration, I ...
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Why are Generative Adversarial Networks classed as unsupervised

The title of the question is basically all I'm asking, but I should explain why GANs don't seem to be unsupervised to me! Here's my understanding of unsupervised learning: Unsupervised learning is ...
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can I propgate machine learning lables in that way?

I have a golden data that I used to build prediction models and then I evaluate the model at the 20% of that golden data and the accuracy is almost excellent. Now, I am planning to use these ...
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is it scientifically correct to label data by model built using golden data?

I am trying to find a labeled dataset for users profiles pictures with their personality traits scores. Unfortunately, I did not find any and therefore, I decided to crawl twitter for public users ...
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When is it okay to label data yourself? (And semi-supervised learning)

i'm pretty new to machine learning so i think this might be a realy basic question. Let's imagine the following scenario: I want to classify projects as either active or inactive. Projects can be ...
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Can this network learn the XOR function?

Let's say I have the following constraints: The architecture is fixed (see image) (note that there are no biases) Activation function for the hidden layer is ReLU There's no activation function for ...
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2answers
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Are data considered to be “events” or “random variables” in machine learning?

I was sitting at a lecture on Naive Bayes, and the speaker, on a slide, said: Given a feature $x = \begin{bmatrix} x_1, \ldots, x_n \end{bmatrix}^T$, the probability of the feature belong to class ...
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Supervised machine learning for dimensionality reduction of control variables in logistic regression

Is it a valid approach to use the predictions of a supervised machine learning (ML) algorithm as a form of dimensionality reduction of control variables in the context of logistic regression? ...
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Number of weights/parameters needed to store a trained Gaussian Support Vector Machines model for binary classification?

I have been trying to make sure I understand this answer right The prompt states: "We trained a SVM classifier which takes input vectors (with N features) and does binary classification using a ...
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Classification: keeping false positive in training set

I am working on a classifier, with a large number of possibles classes, and also a no class class. My training set is made of the output of a hardcoded logic that ...
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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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1answer
55 views

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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28 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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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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31 views

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
34 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
41 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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1answer
35 views

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
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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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153 views

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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246 views

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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2answers
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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
24 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
56 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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0answers
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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^{...