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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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1answer
31 views

What does '1-NN is statistically inconsistent' mean?

I am confused about the fact "1-NN is statistically inconsistent". Why and how?? References https://arxiv.org/pdf/1712.02369.pdf https://www.cs.bgu.ac.il/~karyeh/bayes-consistent-1nn.pdf https://...
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The expected error of 1 nearest Neighbor (1-NN) on large or infinite dataset

I have question regarding the expected error of 1NN. Assume the training set is large enough or infinite. let x' is a test point and r be its nearest point. the probability distribution of two classes(...
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289 views

How to combine multiple signal data in my ML model?

I'm doing a task where I need to work with healthcare data from a few different sources. For example, one is an audio signal recording while another is biometric signal reading such as ECG. Both of ...
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1answer
414 views

Gaussian clusters and original distributions

In Gaussian clustering (i.e. General Mixture Models) we model the data with some clusters. For example, in the below figure, we have two clusters $C_1, C_2$, each of which are modeled with a Gaussian (...
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11 views

Does Calibrating The Model Affect Its Prediction Capabilities

Suppose, I have an imbalanced training set and train a model on it, it will be biased towards the majority class but the probability estimates would be much better calibrated as it would follow the ...
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13 views

Logistic Regression with independently weighted regularization terms [duplicate]

For a standard logistic regression problem I'm interested in examining the impact of individual weighting of the parameters and in general the impact of regularization for completely separable data. ...
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1answer
13 views

Multiple output neurons or One VS All

I would like to classify an input into one of 20 possible categories. I was wondering, what are the positives and drawbacks of using a neural network with 20 output layer neurons (each neuron ...
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1answer
18k views

Benefits of stratified vs random sampling for generating training data in classification

I would like to know if there are any/some advantages of using stratified sampling instead of random sampling, when splitting the original dataset into training and testing set for classification. ...
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Why is accuracy not the best measure for assessing classification models?

This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference. ...
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Should I use stratify parameter for scikit-learn train_test_split while dealing with highly unbalanced dataset?

I have a dataset with over 200000 records. Only 400 of are positive, which makes the data highly unbalanced. I cannot collect more data. At first I trained a decision tree. I used StratifiedKFold and ...
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17 views

Number of Support Vectors increases problem complexity? [on hold]

Why the number of Support Vectors increases a problem complexity? Furthermore increasing C (hard-margin SVM), it has as result decreasing the number of Support Vectors and getting a smaller margin. ...
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2answers
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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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Solving feature bias issues in Learning to Rank with implicit feedback

I have a learning to rank system where implicit feedback (from user clicks) is used to determine +ve and -ve examples for the training. The problem is that (obviously) the learner sees only the top ...
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2answers
155 views

Trying to use historical data to make an event 'non-rare' in a logistic regression model

I am working on a logistic regression model that attempts to predict failure events in a population of devices using the previous 10 days of data (60 features from sensor data). The failure event is ...
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2answers
985 views

Incorporate new unlabeled data into classifier trained on a small set of labeled data

I have a set of 400 labeled samples (8 numeric features) on which I trained a binary classifier. The problem I am facing is that once the classifier is shipped to the users, I will get additional ...
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0answers
9 views

Goodness-of fit indices in CB-SEM AMOS

does any one know whether I should classify the goodness-of fit indices under the structural model or the measurement model? Thanks.
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20 views

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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1answer
10 views

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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3answers
266 views

How should I resample the training and testing set with imbalanced data whilst having meaningful performance metrics?

I have an imbalanced dataset of approx. 200 positive and 800 negative examples. I run nested cross-validation where i=5 and j=5; (i is inner and j is outer). The cross-validation procedure isn't the ...
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494 views

Difference between training and test data distribution

The basic assumption in machine learning is training and test data follows same distribution. But in reality this is highly unlikely. Covariate shift address this issue in which training and test ...
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0answers
11 views

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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3answers
489 views

What's the link between optimization and classification?

I am learning about machine learning and one of the things that still not clear in my mind is how classification is done via optimization? In the couple of papers I read I just don't get how authors ...
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1answer
17 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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1answer
255 views

Would the support vectors in SVM algorithm change with scaling of the functional margin?

Would the Support Vectors in the SVM algorithm change every time that I change the functional margin ? The optimization objective in the SVM algorithm is this - The rest of the SVM optimization ...
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1answer
125 views

Clusters as input for classification

I'm currently performing clustering as a batch job and then in real time I'm assigning new points to cluster whose centroid is closest to new arrived point. The other approach that I see is to ...
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3answers
174 views

Binary classification with imbalanced classes [duplicate]

I have a manufacture dataset of 65 million rows corresponding to 65 millions distinct items. Out of those 65 millions, I have 60,000 of them that failed a certain test, thus I have very imbalanced ...
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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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1answer
32 views

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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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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1k views

Does the cost function for logistic regression use log base 10 or natural logarithm? [duplicate]

I'm trying to implement the cost function to estimate the SVM models I've trained with scikit-learn for a classification problem. Unfortunately, from my course (Andrew Ng's Coursera course on Machine ...
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1answer
15 views

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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1answer
195 views

Bayes decision rule and thresholding

The best possible classification is for a set of samples drawn from any probability distribution is given by the Bayes decision rule. For any distribution, the rule is given by $$ f(x) = 1 \quad\...
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1answer
76 views

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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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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3answers
76 views

Tricky Interview Question [closed]

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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1answer
29 views

how to check the distribution of the training set and testing set are similar

I have been playing the Kaggle Competition and I find there is a situation that the distribution of the training set and testing set are different, so I am wondering how to check the distribution of ...
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0answers
11 views

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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2answers
383 views

Calibration curve of XGBoost for binary classification

I'm working on a binary classification problem, with imbalanced classes (10:1). Since for binary classification, the objective function of XGBoost is ...
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2answers
184 views

Learning the Confidence of a Neural Network

Suppose I want to train a deep neural network for classification. The network takes an input vector $x$, and maps this to an output vector $y$. Now, $x$ is of length $n$ and is in fact composed of a ...
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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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0answers
13 views

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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1answer
2k views

Response is an Integer. Should I use classification or regression?

During a class in my master's in computer science the professor asked us to come up with the best model to predict this particular data set. In it, we are given measurements of the weight and size of ...
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2answers
135 views

What is the origin of the rule of thumb for the number of samples needed machine learning?

I've heard that as a rule of thumb, the number of samples needed for a machine learning algorithm to get accurate results is ten times the number of degrees of freedom. For example, classifying an 8x8 ...
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0answers
4 views

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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0answers
28 views

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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2answers
129 views

What is between regression and ordinal classification (or called ordinal regression)?

There are many articles explaining the difference between regression and ordinal classification, most of them mentioned that regression is for continuous response while ordinal classification is for ...
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1answer
263 views

Text Classification: training on long texts but application on short texts

I have used a linear SVM model to perform a text classification. As I computed the TF-IDF features, which are based on the frequencies of term occurrence in documents, I'm worried about the effect of ...
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1answer
11 views

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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1answer
35 views

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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2answers
2k views

What problem does oversampling, undersampling, and SMOTE solve?

In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...