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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Resampling methods for curves and time series

In the case of imbalanced datasets, different oversampling/downsampling methods exist such as SMOTE, ADASYN, etc. However, this methods mostly simply interpolate in the feature space, treating the ...
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7 views

What are some good strategies for improving a classifier that works well on most of the classes but confuses 2 of them?

I'm having a classifier that tries to classify 5 different classes from a data set. It works pretty well in general, and when I'm plotting the confusion matrix almost all misses are 0, 1 or 2 max 3 ...
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Model for selecting bounding box of interest

I am using the EAST text detection model to find text boxes in an image. In all of these images I am only interested in a certain text box that has always has similar pattern (e.g. 5 digits) but ...
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24 views

Random Forest in R. Dataset not symmetrical [on hold]

I did a couple of training with RandomForest in R for a class problem (event must be 1 or 0). Dataset consists of 10.000 rows and 10 variables (more or less) and each variable is built like ...
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Tagging negative examples in a multi-class classifier?

I'm trying to train a text classier to tag chunks of text with an ID representing the topic being described in the text. I have a large corpus of tagged examples to train it with, and I'm using scikit'...
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24 views

Classifying feature vectors just using a probabilistic classificator for the iris dataset [on hold]

I would like to classify the iris dataset and subsequent vectors from an estimation or guess of the iris dataset distribution. I would like to compare the performance with a neural network. I would ...
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Permutation test for accuracy in multiclass discrimination

It is well known that to assess the significance (i.e. difference from chance) of the performance of a classification analysis one can use a permutation test. In a two-classes case, the procedure is ...
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identifying what has changed in the data year on year

I have a dataset from one year and the 'same' dataset for the next year. I would like to identify what has changed in the datasets between the years. Both datasets have the same columns. I currently ...
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Using groupwise averages of dependent variable as predictors

Let's say I have a binary classification problem with a number of categorical predictors. Is there an issue with creating new features by calculating the group wise average of one or more of the ...
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How does undersampling help classification of imbalanced data set?

In a 2 class classification and a dataset with majority Class 0 and minority Class 1, undersampling of the majority class (Class 0) is sometimes used to aid in classifying the minority class (Class 1)....
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11 views

Evaluation Metrics for a Cost Sensitive Multi-Class Classifier [closed]

Problem statement : I have a 4 class classification problem. There is an associated cost matrix as well. In addition to the evaluation metric accounting for the cost matrix - it should also take ...
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Models for ranking possible classifications by confidence

What are good means of finding the various highest confidences or likelihoods for a (say) hundred possible outcomes of a classification problem? Inputs belong to only one class, unlike document ...
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Difference between class_weight and scale_pos_weight in LightGBM

I have a very imbalanced dataset with the ratio of the positive samples to the negative samples being 1:496. The scoring metric is the f1 score,and my desired model is LightGBM. I am using the sklearn ...
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22 views

How to classify time series trends into 2 groups: “contain seasonality” and “doesn't contain seasonality”

I'm optimizing prediction model for time series data trends. Each trend may have seasonality effect or may not. I want to classify each trend into one of the following groups: "seasonality" or "no ...
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34 views

How should predicted probabilities be interpreted in a binary classification model?

I ran a PLS-DA model with 10-fold cross-validation to classify data in 2 groups (using the Caret package in R). The predicted probabilities are close to 0.5 (the highest propbability is just 0.7). The ...
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6 views

Optimal NN architecture for regression task that benefits from classification

I am aiming to build a NN that would be optimally combining classification and regression. I have reformulated the task such that it would be less abstract and would like to know if the proposed ...
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1answer
30 views

SVM Optimization

Consider a Classification set up where there are $n$ covariates represented by $x_i \in \mathbb{R^{n+1}}$ and $x_{i1} =1$ . While $y_i \in {\{-1,1\}}$ defines the class where $x_i$ belongs to and ...
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13 views

Normalizing data before or after extracting time domain features

I have 100 time series (with 200 instances each) datasets each corresponding to a particular activity. I want to perform supervised classification for the activity. I want to use time domain (time-...
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38 views

How to adjust confidence-interval based on model accuracy?

I have a binary classifier with 94% accuracy on unknown test data. I use that model to classify samples from a large population in order to infer the proportion of positives within the population. I ...
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How to define acceptable false negative/positive rate? Any research you could recommend?

Are there any case studies or research that goes through the process one might take to define acceptable false negative/false positive rates for classification? Say we have a delivery robot and we are ...
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Can we minimize counting cost function for perceptron algorithm?

In perceptron algorithm (the following analysis might apply to other classification algorithms), a smooth approximation of perceptron cost function $$\sum_i^n{\max(0, -y_i\mathbf{w}^T\mathbf{x}_i)}$$ ...
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How reduce categorical level and group labels by finding duplicates [closed]

In a classification multi class problem with high level categorical data , what can be used to reduce the categorical level of features or reduce amount of labels? my data contains duplicates levels ...
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1answer
48 views

R prcomp and KNN different correct classification rate

I'm performing a classification task using KNN and PCA to pre-process the data. The dataset contains 101 continuous variables and the column of the labels (here the link to download the data filebin....
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BiLSTMs with Attention model for Multi-Label Multi-Class Classification

I am trying the modify the BiLSTM with Attention model he used in Course 5 Neural Machine Translation for predicting grades (ranging from O,A+,A,B+,B,C,D,E,F) for multiple subject (approx 9 subjects ) ...
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1answer
23 views

LDA by hand gives different results than MASS

I'm performing LDA by hand in R by following this formula: $\delta_k(x) = x^T\Sigma^-1\mu_k-0.5*\mu_{k}^T\Sigma^-1\mu_k+log(\pi_k)$ where $\pi_k$ is the proportion of the data that's in the group $k$...
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can we use SVM with multiple fuction for some classes?

I am wondering if we can combine the functions in classification with SVM. Suppose we have 4 classes A B C and D. for multi classification problem the SVM (one against all) works as the following ...
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31 views

Random oversampling versus classes weighting for class-imbalanced dataset

I want to train a multi-class classification deep learning model. But my dataset is class-imbalanced. So considering 2 solutions, random oversampling and classes weighting, I have some questions: Do ...
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7 views

How to interpret when using hinge loss performs a lot better than cross-entropy loss in a multi-class clasification problem?"

Given that hinge loss is based on the marginal loss in SVM, is there any reasonable assumption / interpretation one can make on the topology of the dataset, when using multi-class hinge loss ...
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What's the effect of using TF-IDF encoded instead of one-hot encoded categorical data as input to a neural network?

As input into a simple neural network multi-class classifier, I am considering using a variation of the standard one-hot sparse matrix to represent categorical variables. Instead of each element ...
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29 views

Violinplot vs. permutation importance: interpreting differences

I'm analyzing the Titanic dataset, and I've been trying to understand the predictive power of the Age feature relative to passenger survival. My intuition is that younger people had a higher chance to ...
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How can I use receiver operator curve in this SVM classification problem?

Short description of the learning task: I have a corpus containing voice segments annotated with the mean BPM obtained from heart rate recordings. For example, one sample would be like 5 s audio, ...
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1answer
19 views

Accuracy of SVM prediction

I'm trying to build a text classification model with SVM. The training data set consists of 100 string records with a one-to-one mapped response variable which is also a string. I can't split the data ...
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discriminatory power but cofficients not significant [duplicate]

I am trying to fit a logistic regression over 2 features on a dataset of ~200 observations. Distribution of class target is imbalanced, roughly 80:20. I get generally good discriminatory power, if I ...
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Choosing the Right Data Mining Classification Technique

From the different Data Mining tasks, I want to train the Classification. For that, I: Took this dataset (can be used as an example for the answer). Got to know the data (data objects, attribute ...
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1answer
21 views

Is ordinal regression a classification or a ranking problem?

I’m confused, in wikipedia, ordinal regression is also referred as ordinal classification. Which makes sense since ordinal variables are in the end just categorical. On the other hand, ordinal ...
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When to choose regression splines over smoothing splines? [duplicate]

I am currently studying how to model covariates beyond linearity in order to use them with GAMs for regression / classification purposes. Talking about splines, two types were presented: regression ...
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Evaluating predictive uncertainty classification models

I've been using BART (Bayesian Additive Regression Trees) for both regression and classification problems. BART, unlike many other tree based models, provides you with uncertainties on its predictions....
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117 views

How to perform a what-if study using observational data?

A team fit a Random Forest model to a dataset $S=\{\mathbf{x}_i,y_i\}_{i=1}^N$, where $\mathbf{x}$ is a vector of continuous and categorical variables, and $y$ is a binary response. The model has a ...
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Applying TF/IDF to non-text data?

I have a classification problem in which I am supposed to predict the end state of an object based on a set of events it experiences. There are about one thousand possible events and each object is ...
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1answer
21 views

Segmenting video into two

Say I have a collection of videos, each that have an action that occurs at time $t_i$ for video $i$. So I could have $\{v_1, v_2, ..., v_n \}$ with times $\{3, 7, ..., t_n\}$ in seconds, for example....
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1answer
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How does caret resolve ties in the KNN classification? [closed]

I have a multi-class classification problem, in which I'm using caret package k nearest neighbour classifier, (4 classes), which means that an odd number for k won't prevent classification ties. So ...
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2answers
33 views

Binary classification without training data

My goal is to classify students of an online course into two groups: "cheaters" and "non-cheaters". I have some features which can be useful (grade, number of videos watched, some actions with videos, ...
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Machine learning: is the effect of one predictor adjusted for the others?

In machine learning - notably ensemble methods such as random forest, gradient boosting, extreme gradient boosting etc - can we say that the effect obtained for one predictor is ADJUSTED for all other ...
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1answer
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Predicting rank - regression or classifcation

I would like to create a rank predictor (e.g. 1-20). I'm wondering whether I should use classification or regression algorithm for that? As there is ordering between classes then it kind of makes ...
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14 views

Neural network outputs extreme probabilities

I currently have two scenarios that I'm unable to understand: A multi-class neural network classifier: this model's final softmax layer outputs very extreme "probabilities" for each class for the ...
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5 views

Compute radius of multi-dimensional features per class maximizing accuracy

In my setting I have a dataset of $N$ instances, that can be in $K$ different classes. Each instance have $m$ features. In my case, I know that dataset might have some noise. What I'd like to do, is ...
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For microbiome data, is there a way to stratify within Tukey HSD?

I have Shannon diversity metrics for samples that are repeated measures collected over time with different genotypes and I would like to stratify the Tukey by the collection time. ...
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Training a classifier to predict unknown classes

I am building a classifier where I have data from two known classes, but I want to capture any new or "unknown" classes as well in my prediction. However, in my case, I do not have any examples of the ...
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1answer
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Which cross-validation measure of model fit performs best when the objective is probability estimation in classification tasks?

Suppose a binary outcome $Y=0$ or $Y=1$ where $P(Y=1|X)=f(X)$ is a function of $X$. The goal is to estimate $f$ as closely as possible using a classifier that returns a probability estimate (e.g. ...
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Scikit-Learn SVC Porbability Function

I use scikit-learn to train a SVC with 'poly'-Kernel and propability-paramter enabled. Most of the time the prediction and the probability assigned to the prediction is correct. That means: ...