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.

Filter by
Sorted by
Tagged with
0
votes
0answers
17 views

Should I scale time-series features for supervised learning classification?

I couldn't find an answer to this in the archives so posting this here. I am currently building out a supervised-learning / classification pipeline for time series forecasting (e.g. predicting the ...
0
votes
0answers
8 views

What is the difference between weak supervision and distant supervision? Is it just me or is there no clear-cut definition?

I've been trying to pin down what would make each category stand out. My understanding is: Weak supervision seems to be a broader term for distant supervision. The papers that I've read seem to ...
0
votes
1answer
27 views

Learning HMM parameters by counting?

In 8.4.3 of the book Speech and Language Processing: An introduction to natural language processing, the two matrices transition probabilities and emission probabilities can be learned by counting as ...
0
votes
0answers
13 views

Metric for comparing supervised and unsupervised model

I'm searching on how to compare (validate) a supervised learning model to an unsupervised one. Let's say I have a supervised model for fault diagnosis which can tell me how accurate it is to predict ...
0
votes
1answer
24 views

Survival function as output label for supervised dimensionality reduction [closed]

I would like to do supervised dimensionality reduction with UMAP for survival analysis. As both the time to event as well as the event itself are of interest for survival analysis, I calculated the ...
1
vote
1answer
17 views

reflectance value ranges from -3.40282e38 to 3.40282e38 in qgis [closed]

reflectance value ranges from -3.40282e38 to 3.40282e38 in qgis atmoshpherically corrected image, I want my value to range between 0-1 how can i fix this?
1
vote
0answers
44 views

What are some machine learning frameworks for supervised clustering?

I have a task where I need to take "data points" which consist of collections of items. Each item needs to be categorised according to predefined categories. That's the easy part - my ...
1
vote
0answers
19 views

When we up-sample the training set, don't we introduce selection bias?

When doing supervised machine learning in the health or medical domains, we often have a target class that is relatively rare (e.g., prevalence 1-10% of cases). There are a few techniques we can do to ...
1
vote
0answers
4 views

Random Survival Forests (RSF) for longitudinal data and multivariate outcomes

I am trying to find out if there is any validated model out there that covers RSF with time-varying covariates and multivariate outcomes (such as competing risks). Would this just be an extension to ...
1
vote
1answer
22 views

Evaluating Supervised Model Performance Against a Baseline

My question is regarding how I can interpret the performance of a supervised ML task relative to a baseline estimator. I have run a supervised ML as a regression, and used K-fold CV to evaluate ...
2
votes
1answer
62 views

What is the difference between the risk function used in Bayesian inference and the one used in supervised learning?

In the context of Bayesian inference, given the random parameter $\Theta$, the observed data $\mathcal{D} = \{x_1,x_2,\dots,x_N\}$, the posterior $p(\theta\mid \mathcal{D})$, the estimator $\hat\...
2
votes
1answer
62 views

What is the link between probabilistic predictions and Bayes optimum decisions?

Frank Harrell writes in one of our community wikis about the link between "Bayes optimum decision" and the link to probabilistic predictions (and, thus, one of his favorite topics in proper ...
1
vote
0answers
22 views

Evaluating a true classifier e.g., pregnancy test

Most alleged "classifiers" give probabilities of class membership. One can use a threshold to map those probabilities to discrete categories, but statisticians are in favor of direct ...
0
votes
0answers
11 views

Prediction of Binary Tuples

Which statistical model could useful for the following case? Framework: an chat-and-call application like Whatsapp where users can use 3 features: chat with a contact send a message in a chat-group ...
0
votes
0answers
8 views

Neural Network Architecture for Image to Image Supervised Learning?

I have a task where I would like to create a supervised learning model where each training record of X,y is a pair of images. e.g. learning a transformation from an image to an image. Is there a ...
0
votes
0answers
111 views

How to compute derivative of loss function with respect to weights in forward neural networks

Consider the feature space $\mathcal{X}=\mathbb R^{d}$ and $\mathcal{Y}=\{1,...,c\}$ such that $c > 2$. We consider some activation function $\alpha: \mathbb R^{c} \to \mathbb R^{c}$ and out weight ...
0
votes
0answers
88 views

Distinction between ranger and randomForest packages/functions?

I know that ranger package of random forest is useful in high-dimensional data and it saves computational time and memory compare to the randomForest. My Question is: Is there any other differences or ...
3
votes
1answer
77 views

How much of neural network overconfidence in predictions can be attributed to modelers optimizing threshold-based metrics?

Neural network "classifiers" output probability scores, and when they are optimized via crossentropy loss (common) or another proper scoring rule, they are optimized in expectation by the ...
2
votes
1answer
13 views

Can SML with class labels optimize directly the class probabilities instead of class assignment accuracy?

This is, for now, a purely theoretical question. I am interested in using Supervised Machine Learning to predict, for each test observation, what is the probability it corresponds to each of the ...
6
votes
2answers
72 views

Is there something like a confusion matrix for a probabilistic score rather than categories?

Imagine we have pictures of three animals: dogs, cats, and horses. We train our image classifier and get a confusion matrix, noticing that the model tends to predict that dogs are horses. But then we ...
4
votes
3answers
381 views

Why isn't the ROC curve naturally plotted in 3D? [duplicate]

Something that really confuses me with how ROC plots are generated is that, according to Wikipedia: The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (...
2
votes
0answers
36 views

R Squared (OOB) and R Square from correlation of prediction of test set is different?

I'm using simulated data and fitting Random Forest model for regression on a training dataset. What is confusing me is that after running Random Forest, I got R Squared (OOB) equal to 0.14. But when I ...
2
votes
0answers
15 views

Is it harder to spoof (adversarial examples) a model trained to optimize a proper scoring rule than an improper scoring rule?

The way I figure, if we train a model to stick points on the correct side of a threshold like $0.5$, then all we have to do is tweak a $1$ with a predicted probability of $0.51$ to give a predicted ...
2
votes
0answers
21 views

Event detection in multivariate time series

I've have the following multivariate one second time series composed of that I can manually label and its based on sudden value changes (increase)from zone 1 to zone 2 and (decrease) from zone 2 to 3. ...
0
votes
1answer
16 views

Can Simple Multiple Regression be applied when you have a Training Set where the number of features is greater than the number of examples? [duplicate]

Suppose we have a Training Set $X$ of size $n\times d$, where $n$ represents the number of examples and $d$ represents the number of features. Assume that $d>n$, so the number of features is ...
1
vote
1answer
66 views

What's the purpose of learning rate in sklearn AdaBoost implementation

We know that sklearn's implemenation of AdaBoost algorithm uses DecisionTreeClassifier as the base learner. Conceptually, ...
0
votes
1answer
56 views

Multiclassification: precision-recall from scratch vs sklearn

I would like to know if there´s any issue behind using sklearn's precision/recall metric functions and coding up from scratch in ...
2
votes
0answers
18 views

How to extract simple shapes from a feature map?

I am working on image parsing project. I want to find a way to automatically parse an object into a list or a graph of simpler shapes. Is there any practical information on how to do so? So far I took ...
0
votes
0answers
11 views

Perceptron learning rule - Recover input patterns

Doing exercises in my Deep Learning book I'm stuck on this following exercise as the correct answer is marked without any explaination: Using classical perceptron learning rule starting with W0 = [2.4 ...
3
votes
1answer
33 views

Comparing the accuracy of binary classifiers using iterated cross-validation

Let's say that you want to compare two binary classifiers (e.g., LDA and linear SVM) for a given research question, the question being "which one will probably perform best for the problem at ...
0
votes
0answers
28 views

Does this problem requires Supervised Learning or Unsupervised Learning

I have 50 Features in a Dataset to predict 1 Variable "Units Sold". I am currently using XGBoost model (Supervised Learning) to train all these 50 Features and the accuracy of the model on ...
1
vote
0answers
58 views

When should one look at sensitivity vs. specificity instead of precision vs. recall?

The precision vs. recall tradeoff is the most common tradeoff evaluated while developing models, but sensitivity vs. specificity addresses a similar issue. When should one of these pairs of metrics be ...
3
votes
2answers
36 views

Data-efficiency vs sample-efficiency

I noticed that in the context of RL, people call the ability to learn from little data "sample-efficiency". However, in the context of supervised learning, it is called "data-efficiency&...
0
votes
0answers
8 views

K-NN machine learning model in R - model is trained and accurate but how to use it?

For the game 'league of legends', I want to categorize observationa/matches to the label 'Lose' or 'Won'. In order to do that I make use of the k-nn machine learning model. It works as expected (see ...
2
votes
0answers
16 views

Subsampling the "right" amout of data to train an ML model

I am training a machine learning model (i.e., a classifier) on a large dataset. I know that I can get the same results using less data (about 30%) but I would like to avoid the trial and error process ...
0
votes
0answers
6 views

Is it normal for an SVM to take longer to train with unbalanced data than balanced data?

I have experienced much longer training times with SVMs when data for a binary classification task is unbalanced vs balanced, even if the amount of data is the exact same between the two training ...
0
votes
0answers
16 views

Model Suggestions: Object Tracking for Ants

I hope it's going well. For some time now I have been training YOLOv3 to detect the bodies and heads of bull ants, a large type of ant from the genus Myrmecia. The goal is to be able to extract the ...
0
votes
0answers
12 views

Are there some supervised machine learning methods that learn to rank the features?

For example, given training data/features $$ \mathbf{X}^{(1)} = \{\mathbf{x}_1^{(1)}, \mathbf{x}_2^{(1)}, \mathbf{x}_3^{(1)}, \dots, \mathbf{x}_{l_1}^{(1)}\} \\ \mathbf{X}^{(2)} = \{\mathbf{x}_1^{(2)},...
1
vote
1answer
42 views

What is it called when you find the best fit in an RKHS to some training data?

Suppose I have a series of labelled training inputs $(x_i, y_i)$, and a kernel function $k$ on the input domain, with a corresponding RKHS $H$. Now form the Gram matrix $A$, where $A_{ij}=k(x_i, x_j)$....
0
votes
0answers
6 views

Is supervised learning harder under multiple-labels than when labels are mutually exclusive?

It is common to encounter problems that involve some form of multi-class supervised learning. Within this category, there are two possibilities. One that the classes are mutually exclusive (...
0
votes
0answers
9 views

Is there a need to standardise training and test sets separately for binary classification problems? [duplicate]

When setting up an ML framework for binary classification do we need to standardize our training and test sets separately? This answer claims to standardize separately (although never states the ...
3
votes
0answers
29 views

Question about minimising empirical loss by gradient descent

Say we wanted to learn $f_{\theta}(\pmb{x})=y$, with a loss function $L(f_{\theta}(\pmb{x}),y)$. We often want to choose $\theta$ which minimises the empirical loss, as the exact loss isn't available ...
30
votes
5answers
3k views

How can you account for COVID-19 in your models?

How are you dealing with the coronavirus "event" in your machine learning models? Let's say you used to predict the number of sales each month. The virus affected your results last year and ...
0
votes
0answers
5 views

How can I learn structural positions of elements from dataset?

I have a dataset of multiple coordinates of points and their label taken from images . ...
0
votes
0answers
16 views

Why are random forest predictions over a smaller range than the true target variable? [duplicate]

I have been running a random forest regression to predict a normalized target variable (where scores range between 0 and 1). However, whilst I am getting a reasonable level of overall performance, ...
0
votes
0answers
31 views

Random vs deterministic predictors in regression

I am reading Elements of Statistical Learning (ESL) and trying to have more of a grasp of machine learning techniques. I am a little bit confused about when to treat predictors as fixed, and when to ...
2
votes
1answer
23 views

Measuring performance using cross-validation on the full dataset after training on a subset of that dataset?

In my Python code below, I'm measuring the performance of my model using cross-validation. I'm doing it with my full dataset (X,y). This is after I trained on a subset of that data (X_train, y_train). ...
3
votes
1answer
56 views

How does using PCA speed up supervised learning?

In his popular course, Andrew Ng mentions using PCA to speed up supervised learning (Lecture 14.7). The basic idea is dimensionality reduction, wherein the extremely high-dimensional input features $\{...
0
votes
0answers
14 views

Predicting the Variance of the Residuals

Regression is the task of modeling the response $r$ to the exploratory set of variables $X$ such that: $r=f(X)+ \epsilon$ Assume the regression function $f(X)$ is already known, and for any future ...
0
votes
0answers
23 views

Theory Question: Machine Learning & Feature Correlation to Label

I have a theoretical question about creating an artificial feature based off of a binary classification label, and then adding it into my feature set to run my analysis. First, let me show you what I ...

1
2 3 4 5
12