Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

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.

0
votes
1answer
25 views

Trying to Understand the Statistical Notation [on hold]

Does x need to be randomly sampled from a normal population and what is d?
0
votes
0answers
28 views

Compering Facebook Prophet to supervised learning [on hold]

I'm working on a time series forecasting on one of my projects. I would like to compare the MAE (or any other scoring) of two different models, one is facebook prophet and the other one is ...
1
vote
0answers
16 views

Forecasting time series data using EEMD based SVM?

Splitting of Dataset: Dataset = Train1 + Test1 EEMD(Train1) = train1 + test1 I am forecasting on time series data("Dataset") using SVM. First I found the Intrinic Mode Function(IMF) of time series ...
5
votes
1answer
182 views

How would someone use curves as an input to a supervised learning model?

I was asked this question during a test and couldn't figure out the answer: You have a set of curves against time $X_i(t)$ that you want to use as input to a supervised learning model. The curves ...
0
votes
1answer
14 views

How to interpret a high sensitivity and low specificity using svm classifier?

I want to have your opinion on the performance of my SVM classifier (k-fold cross validated): Classification of class1(n=45)/class2(n=86) Accuracy: 65.4% Sensitivity: 88.2% Specificity: 22.2% AUC:...
1
vote
0answers
43 views

Recognizing whether a written and spoken number is the same

For our ML assignment we have three datasets. The challenge is about checking whether a written and spoken number refer to the same number. We're using the MNIST dataset with handwritten numbers, and ...
0
votes
0answers
6 views

Can normalized values and original values be combined in a feature vector classification?

I standardized feature vector before SVM classification in MATLAB. The feature vector consists of time domain signal features and its normalizations i.e., feature vector is a combination of actual ...
0
votes
0answers
11 views

When is the sum of model predictions the prediction of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
0
votes
0answers
25 views

Looking for ways to transform time-series data recorded from object movement into equation describing the movement direction of the object

Looking for some time-series data transformation advice! I want to know what's the best way to transform data of 9-tuples time series data of IMU (Inertia Measurement Unit) sensor, recorded from a ...
0
votes
0answers
9 views

Supervised learning vs Regularity based learning

I have some confusion about regularity based learning and supervised learning. Are they in essence, not the same thing? We have some labelled data, and our algorithms are structured based on learning ...
0
votes
0answers
9 views

What is the best method using supervised learning to label images with single-pixel-level defining features?

The images are in later form of size 170 where each index could be a -1, 0, or 1. The images are multi-labeled, with 8 possible labels. The issue is that there are often only slight differences ...
1
vote
1answer
18 views

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 ...
0
votes
0answers
5 views

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 ...
4
votes
1answer
35 views

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 ...
0
votes
1answer
20 views

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 ...
3
votes
1answer
38 views

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-...
0
votes
0answers
15 views

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 ...
3
votes
1answer
200 views

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 ...
0
votes
0answers
8 views

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%...
0
votes
0answers
63 views

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 ...
3
votes
1answer
25 views

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 ...
0
votes
0answers
7 views

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 ...
3
votes
2answers
76 views

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 ...
0
votes
1answer
29 views

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 ...
2
votes
0answers
118 views

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 ...
1
vote
2answers
40 views

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 ...
0
votes
0answers
29 views

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? ...
0
votes
0answers
16 views

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 ...
0
votes
1answer
51 views

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 ...
1
vote
0answers
36 views

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 ...
0
votes
0answers
17 views

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)...
0
votes
1answer
58 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 ...
0
votes
1answer
30 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 $\boldsymbol\alpha$...
0
votes
0answers
51 views

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. ...
0
votes
0answers
29 views

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.
3
votes
1answer
37 views

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 ...
0
votes
0answers
40 views

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)?
-1
votes
1answer
34 views

Parallel Bagging in supervised learning

How can we parallelize Bootstrap aggregation, a.k.a Bagging, i.e. train all classifiers at once?
1
vote
1answer
28 views

Advantages of dual formulation

Why do we solve the dual form of the SVM in practice to obtain a classifier instead of the primal?
3
votes
3answers
54 views

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 ...
0
votes
0answers
32 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 ...
1
vote
1answer
42 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 ...
0
votes
1answer
48 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: ...
2
votes
1answer
37 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 ...
1
vote
1answer
59 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, ...
1
vote
0answers
21 views

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 ...
0
votes
0answers
58 views

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/...
0
votes
3answers
229 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...
1
vote
0answers
309 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 ...
7
votes
1answer
74 views

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 $y$ (...