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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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 ...
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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 ...
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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)},...
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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 $A_{ij}=k(x_i, x_j)$....
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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 (...
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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 ...
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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 ...
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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 ...
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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 . ...
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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, ...
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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 ...
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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). ...
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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 $\{...
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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 ...
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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 ...
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Best approach for energy demand forecasting

I am trying to predict the amount of energy demand(Wh) of the next two weeks per hour. The dataset I have, contains each hour of each day since 2019 of the energy demand, is something like this: ...
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The impact of Normalization when training MLP

I come across a problem where I trained two MLPs using the same dataset, but one was trained using the raw data and the second one was trained using the normalized version of the dataset. In this case,...
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LDA QDA with constant value of 0 in dataframe

I have an issue with LDA and QDA methods. I noticed that when I use these methods on a dataframe full of dummy variables ( possibility of columns with only with the value of 0) , these methods don't ...
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On quantifying the amount of information per example provided to the model in Supervised vs Self-supervised learning

I've seen Yann Lecun in his self-supervised learning talks talking about how traditional supervised learning (Classification setting) by attributing a class out of N classes to each example the ...
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Find optimal training dataset after concept drift

There are many strategies how to detect a concept drift or model drift, like when there was a major change in the underlying process so that the model becomes invalid. It can be an abrupt change or it ...
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What is the difference between Transfer learning and Trained/Supervised machine learning?

I am trying to understand the difference between the supervised / labelled machine learning and the trasnfer learning. From my reading and understanding they are similar. Because in both cases we use ...
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Topic modeling for regression

Is there a way to influence the way topics are created with topic modelling in the sense that the topics also reflect their influence on the target variable of a machine learning problem? I have a ...
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Supervised Discretization based on multiple time series

I'm having multiple time series observations $X_{k1},..., X_{kt}$ with a single binary response $Y_k$ for each time series $X_{kj}$ for $j = 1, ..., t$ (Multivariate Time series). Now, I want to make ...
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Q-function in Q-Learning

I ran into solved old-exam question as follows: My notes tell me that option b is correct but I think option d is correct. is there any idea why (b) is correct?
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Information gain of the root node

Recently I saw this question and answer as attached in following image Anyone can add details how this solution achieved?
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How to handle errors in your target data?

For the sake of the question, assume I'm presented with a simple classification task (for simplicity, let's assume binary classification). We are given a feature matrix $X$ and a target vector $y$ of ...
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Why does a function being smoother make it more likely?

I am currently studying the textbook Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams. Chapter 1 Introduction says the following: Given this training ...
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Overfitting during last model training stage

To select, tune, train an ML model I used the following stages: Split data in train / hold-out dataset Perform nested cross validation using the train data only (loop with several models with a grid ...
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Which are some formal approaches for predicting multiple binary time series?

I have 10000 roughly similar individuals. For each individual I've got a response (binary time series), 200 explanatory features (also time series), and 10 static features that represent ...
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3answers
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Confusion about Understanding Supervised Learning as Bayesian Inference

I am going through a lecture that is explaining how supervised learning can be thought of from a Bayesian perspective, where we are trying to maximize log p(theta | data). I am confused as to what the ...
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Using Survival Analysis to Estimate Loan Prepayment Behavior/Speeds

I have a dataset which consists of monthly account level information for fixed rate loans that were originated on or after January 2013. The account level information includes the account id, ...
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Semi Supervised learning vs Supervised

I am trying to understand the mathematical properties of supervised learning and semi-supervised learning. Let us consider the case for the mean $\mu$. Then the supervised learning estimator can just ...
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How is stratified sampling better than sampling equally from all classes while crossvalidating?

I can see that stratified sampling helps in maintaining the same class distribution in the training set as in the original dataset. However, my understanding is that ideally, the model should be ...
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I have a classifier, now I want to identify best parameters

I have data which I have classified using many supervised classifiers (using matlabs classification learner). I am classifying a Pass/Failed test vector with 50 different variables\features. I have ...
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Confidence Interval as a feature in supervised learning?

Imagine a model that predicts the probability that a given online ad will be clicked by a given user. One of the features is the click-through rate (CTR) of the user (...
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Why does regularization wreck orthogonality of predictions and residuals in linear regression?

Following up on this question... In ordinary least squares, the predictions and residuals are orthogonal. $$\sum_{i=1}^n\hat{y}_i (y_i - \hat{y}_i) = 0$$ If we estimate the regression coefficients ...
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1answer
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Proper Scoring Rule in Optical Character Recognition

Cross Validated likes to promote proper scoring rules in "classification" problems. That is, get accurate probability predictions. Then make the classifications, taking into account the cost ...
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Which machine learning model? For small dataset and very long feature vectors [closed]

I've got two questions about which model might be the best for the assumption described below: In the assumption, we are given various of compounds, wanting to determine the result of the reaction of ...
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Changing L2 regularization constant in logistic regression proportionally to the number of columns/rows in the dataset

I'm trying to use scikit LogisticRegression to solve a multiclass text classification problem with variying number of columns (unigrams) in the trainging datasets. From what I understood, L2 ...
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Multi-regression model validation

I'm a new-bee in the ML modelling and have created a multi-linear regression model. I have got the rmse score for the model as approximately 5. How am I suppose to interpret this?
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Is there a machine learning method for matching similar groupings?

I have a problem where I have rows/samples that are grouped together and each sample has a specific label (my data is genetic with genes being the samples and they are grouped together in the genome ...
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Square loss for “big data”

Let’s set up a supervised learning problem with $p$ predictors and $n$ observations. The response variable is univariate. The problem can be regression or classification, though I think a ...
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What's the best strategy to fill NAs for a predictor in supervised learning e.g. SVM?

What's the best strategy to fill NAs for a predictor in supervised learning e.g. SVM? I have monthly data for all other predictors since 1963 and for one predictor I have data since 1990 only. So I ...
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What approach should be used to model changing conversion rates?

Suppose I want to predict conversion rates of products in an eCommerce web site. Conversion rates can change over time due to changing market condition in addition to seasonality. I can build a ...
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159 views

How to determine equation of hyperplane for SVM?

Assume we have only two features in our training dataset that is already classified into class C1 and class C2. The transposes of the feature vectors are given below for each class: C1: [2 6], [1 1], [...
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Extract Keyword/Concept From Column Description Using NLP

Suppose in my database, each table has a description associated with each column and I want to further extract keyword or key concept from the description. For example, mean of transaction amount in ...
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Would the softmax classifier ever yield equal probabilities for more than 1 class?

The question might be quite straightforward but I can't seem to be find any relevant resources from Google. All the sources I found are focused on explaining difference between softmax and sigmoid ...
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Best method to handle unknown class in supervised classification

I have a training dataset, where the records are labelled into 3 classes: A, B and C. My testing dataset consists of records that belong to classes A, B, C and records that do not belong to any of the ...
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General implications of low entropy for a dataset

I am fairly familiar with entropy, which quantifies uncertainty/surprisal of a random variable. In my case, I have a corpus where I can use empirical word frequencies to estimate entropy of the entire ...
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ROC Curve for unbounded scores

Say I have a classifier that assigns a score to an image based on whether it has a cat in it. The higher the score, the more likely there's a cat in it. But for this classifier, the value of the score ...

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