Questions tagged [classification]

Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.

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13 views

Binary classification on dataset with similar distribution

I am working on Binary clasification. The independent variable have very low correlation(< 0.1) with the dependent variable. Both the classes have similar distribution in features(percentile etc). ...
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How to measure dissimilarity between two classes in terms of a feature?

I have a data set in which each data point has a label y and a feature vector $X=(x_0, x_1, ...)'$. What I need is: given two classes, a and b, and a feature $x_i$, how much do members of the two ...
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weak learning of 3-piece classifiers using decision stumps

I have a question about Example 10.1 in Shalev-Shwartz and Ben-David's "Understanding Machine Learning." The example means to illustrate weak learning of 3-piece classifiers $\mathcal H$ using ...
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After training, how to assess the importance of each sample for a decoder

Lets say I have 100 data samples with 30 features of a binary event which I can classify with a linear support vector machine to ~80% accuracy assessed with 10-fold cross validation. My question is ...
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Good strategy for spatial sampling and accuracy assessment

I have to classify satellite images using machine learning algorithm, preferably Random Forest.I have read in several papers that sampling should be balanced and in some papers I found that training ...
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1answer
16 views

ROC score for binary classification problem, where the predictions are either 0 or 1

For problems with binary classification, roc auc curve or roc auc score is often used to rate a model. But does the ROC ACC make sense in the context of a binary classification model that outputs only ...
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How to properly use batch normalization during inference

I am trying to manually implement calculations of the image classification process using pre-trained weights from the MobilenetV2 network. I know how to apply filter weights to channels, but do not ...
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Softmax + CE vs Sigmoid + BCE for batched training with negative sampling, for training similarity properties

This is a follow up to this question Machine Learning: Should I use a categorical cross entropy or binary cross entropy loss for binary predictions? I am training cos similarity properties for ...
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Bayes Decision Theory With 3 Classes

I'm trying to create a Bayes classificator in 1 dimension with 3 classes. I have created the following graph, where you can see that from zero to $x_{bnd1}$ is the first area $R1$, then from $x_{bnd1}$...
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How can we include hourly traffic series data in the rows of train data set for training?

I have a classification problem where I am planning to use hourly traffic data for a day. Is there any way to compress it? instead of creating 24 predictors which account for hourly traffic?
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Method for detecting previously unseen class

Is there any common practice for detecting a new class, or data associated with an previously unseen event? I'm doing some research into speech recognition, and I'm trying to detect when a speech ...
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Tricky Interview Question [on hold]

I was recently given an interview, and given the following scenario: You have one classification problem to solve. You can use either of the following 1) linear regression algorithm 2) Neural ...
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Minimum number of obs. for machine learning and training/test sets?

Are there a minimum number of observations for ML techniques (classification, regression) in psychology/cognitive neuroscience? In particular for training and test datasets? I found this article for ...
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Using Bayesan statistics to improve classification task

I have a question regarding a classification problem that I think it can be addressed using Bayesan statistics, but I am not familiar with Bayesian statistics and it would be great to get some support....
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interpretation of precision and recall when oversampling or undersampling in mlr

I balance my dataset with e.g. cpoUndersample() from mlrCPO Does this balance my test-set as well? This is important because ...
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Clarifying which elements of the input to neural network should be changed to output a higher probability to the particular label

In classification problems using neural networks, Is there a method to clarify which elements of the input should be changed to output a higher probability to the particular label? A method like "...
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Can logistic regression be used to predict future classifications?

I am trying to predict if a doctor is likely to switch from prescribing Drug A to Drug B. Based on my understanding of logistic regression, you can use the independent variables to determine the ...
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64 views
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To improve the posterior belief receiving a time-series from a fixed data source

Let data $\mathbf{X}\in \mathbb{R}^d$ come from one of the $K$ possible sources $\mathsf{S}\in \{1,2,...,K\}$. The true $\mathsf{S}$ is unknown but it is fixed. The main task is to infer the true ...
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Detecting overfitting on multi-class classification model

I have seen this question asked in one flavor or another, but I'm looking for clarity on a more specific piece. I have two text classification models: Model A: train score=88%, test score=76% Model ...
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Gaps in time-series for LSTM classification model

I am using an Long short-term memory (LSTM) recurrent neural network model to perform classification of accelerometer sensor data. The experiments (for collecting the data) were run a few months apart ...
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Self Organizing Maps - Mapping a single vs more layers

Suppose we train a Self Organizing Map (SOM) with two input layers, meaning we have the following situation: We have a vector $x=(x_1,...,x_n)\in\mathbb{R}^n$ which could represent biometric ...
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What to do if my trained and tested model classifies incorrectly some data? [closed]

My professor gave my class an interesting question: after training a model (the algorithm is not specified) to classify if a 2D point is within a certain area or not, our client tells us that after ...
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Compare two classifiers that works on two different datasets

I have 2 activity-recognition classifiers working on two different dataset (representing different repetitions of the same movement). Since the performance values are obtained from different samples, ...
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Is a multilayer perceptron feasible/advisable when the number of samples for each class can be expected to be 100 or fewer?

I am a beginner, and trying to understand which parameters to choose for a machine learning task I'd like to solve with a multilayer perceptron/NN. I believe it compares to MNIST in a way, but has ...
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21 views

Is it normal to have same testing accuracy? [closed]

I am new to neural networks. When I ran a TensorFlow model using RNN, I notice that some testing accuracy remains the same for many epochs. Why is this happening? What is wrong? In addition, ...
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Naive Bayes + KDE = Lazy?

If I in Naive Bayes use Kernel Density Estimation to estimate logarithms of the conditional probabilities of the attributes in each class $\ln p(x_j|C_k)$ can we consider this classifier to be an ...
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42 views

AUC or $R^2$/RMSE for binary classification

I am using doing a binary classification to classify things 0 or 1 using a set of features with LightGBM and XGBoost. Both models give AUC scores roughly in the <...
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Derive the inference equation for Bernoulli-distributed two-class classification problem

Consider the following probabilistic model: We have a training set of X = x1.....xn, where each sample consists of m binary features. tn=1 corresponds to C1 and tn=0 to C2. I already derived the ...
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Why is softmax considered counter-intuitive for multi-label classification?

In the FB paper on Instagram multi-label classification (Exploring the Limits of Weakly Supervised Pretraining), the authors characterize as "counter-intuitive" their finding that softmax + ...
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1answer
36 views

Multi class classification using Naive Bayes

I have components basically divided into two main categories. AWS and Azure. For eg: ...
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1answer
14 views

Testing machine learning models using a different set of labels

I have a dataset with 119 features and 228 samples. I have trained my classifier using a particular set of labels (0 and 1). Now I want to test how the model is performing to external data. There is ...
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1answer
32 views

Splitting dataset with respect to categorical variable

Assume that we have a data set with some features and a goal is to perform a classification. Let's assume that a dataset is moderately large compare to the total number of features. Next, assume that ...
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Shape classification by color - computer vision

I have task of classification of polygons. These shapes may differ only by color, or be transformed by linear transformation (scaled, rotated). I am using ORB for separating different polygons, but it ...
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What is the difference of “normal” F1 and macro average F1 score with binary classification

Please note that I always talk about binary classification here. I do not speak about multi class classification. In case of unbalanced binary datasets it is a good practice to use F1 score. While ...
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Finding 95% CI for logistic output of Neural Network

I have an mlp with a logistic output between 0 and 1 for a binary classification task. With each run, I have to adjust my threshold for minimizing the misclassifications. My question is to present my ...
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Getting a strange error about dimnames when using caret train function in R [migrated]

Here is the data: https://archive.ics.uci.edu/ml/datasets/Abalone. My code ...
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1answer
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How does size of test set affect the performance of a model?

My data set is divided into 80:20 train and test...i have performed 10 fold cross validation on the train data set and tested the 20 % dataset on each iteration ( so that test set is not touched while ...
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111 views

When does multi-task learning make more sense than multi-label classification?

As part of writing a book on machine learning, I am creating an extreme multi-label stack overflow question tagger for thousands of tags with varying numbers of training examples and I’ve approached ...
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Naive Bayes: why not to compute the likelihood probability directly? [duplicate]

A Bayes classifier assigns to an observation $X$ the class $Y$ that maximizes: $P(Y|X) \varpropto P(Y)P(X|Y)$ I wonder why not to estimate both $P(Y)$ and $P(X|Y)$ directly from the training data ...
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Multiclass Classification with Categorical Predictors

I have a data set consisting of 954 observations of 14 variables. The target variable is categorical with 3 classes. However, 12 out of the 13 predictors are categorical variables, so I have converted ...
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1answer
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Which evaluation metrics are mutually redundant?

Suppose we are given a confusion matrix for a binary classification: tp, fp fn, tn Now, there are lots of evaluation metrics: POD (probability of detection, aka hit rate, ...
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How to quantify performance of subclasses?

I have a dataset of $N$ points. Each point $p_n$ has an associated label $l_n$ which is either $0$ or $1$, $n=1$ to $N$. Let $\overline{l}$ be the vector of all $l_n$ stacked together. Say every point ...
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Need for dimensionality reduction in Breast Cancer Dataset

I was attempting to analyze the Wisconsin Breast Cancer Diagnostic dataset. Have a couple of questions / doubts. Per the attached paper, the performance metrics were worse after dimensionality ...
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68 views

what steps to take to get better performance

I have a data frame like this : ...
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18 views

class-specific neural networks vs overall network

Let's say I am working in a classification setting, like MNIST for instance, and imagine that I have 10 neural networks which are all slightly different and each perform very well on one unique class. ...
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Support vector in SVM

As I have studied that support vector in SVM, is either on margin on inside margin. But during an exercise on simulated set, I am getting a support vector very far away from margin. Here is the ...
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How to model dependency between input features when building a classifier

I have dataset with the shape of (1000,20) (1000 rows, 20 features) and I want to build a classifier for it. However, most sk-learn algorithms assume the these 20 features are independent. In my ...
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Is doing oversampling on train set and undersampling on test set correct?

I have an imbalanced dataset (95% in class 0 and 5% in class 1) and I am using machine learning for classification. The AUC(Area under ROC curve) was high (about 0.86) but AUPRC(Area under precision-...
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Hard classification problem - test set accuracy seems to cap out at a certain value regardless of architecture [duplicate]

I've been working on a hard binary classification problem (50-50 split between classes). I've tried a variety of different network architectures and training schemes - dropout, no dropout, batch ...
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Applying logistic regression with response and expected response

I hope my title is phrased correctly, otherwise feel free to rephrase it. This is my first time working with such a data set and i'm trying to understand if a method i'm using is correct. Here is a ...