Questions tagged [classification]

An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.

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Decision tree Why is Gini index only used for binary choices?

I would like to understand why "Gini index operates on the categorical target variables in terms of “success” or “failure” and performs only binary split" ? Why it would not be possible to ...
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Determining the effect of combinations of independent variables (customer charateristics) on dependent variables (customer value)

I have lots of transactional and demographic (etc.) data about my customers and I want to understand: "What are the characteristics (age, profession etc.) of valuable customers?" To do this ...
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Predictive Recency-Frequency-Monetary (RFM) through Classification of Customer Charateristics

I have an RFM model that I use to segment customers based on RFM score. What I would like to do is: Understand more about the ...
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1answer
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Confidence intervals for evaluation on test set

I'm wondering what the "best practise" approach is for finding confidence intervals when evaluation the performance of a classifier on the test set. As far as I can see, there are two ...
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How can I deal with tiny categories?

I'm playing around with UCI Bank Marketing Dataset. So, there is a categorical variable named default which tells us if client "has credit in default". ...
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1answer
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What does "S" in Shannon's entropy stands for?

I see many machine learning texts using the following notation to represent Shannon's entropy in classification/supervised learning contexts: $$ H(S) = \sum_{i \in Y}p_i \log(p_i) $$ Where $p_i$ is ...
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Improve model accuracy in multi-classification problem

I use a MLP to classify three different classes A, B, C. The loss function I use is categorical cross entropy and the optimiser ...
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input of Auto-Encoder as a feature extraction for training is similar to data that we use later for a classification model?

I have a data set of images, for example, 200 images, I want to use Autoencoder as a feature compressor. I use for example 150 for train the autoencoder and 50 for evaluation. after train and evaluate ...
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Why is the leave-one-out error for support vector machines equal to the number of support vectors divided by the number of training examples?

Elementary question about support vector machines. Given a support vector machine classifier and a linearly separable dataset. Why is the leave-one-out cross validation error said to be bounded by the ...
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Random Forest to Identify Page: Feature Selection

I am new to machine learning. I know of a project that used Random Forest to identify the type of pages in financial reports, e.g., identify if a page is the cash flow or income statement. The ...
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How is a single classifier fitted on AdaBoost?

The AdaBoost algorithm is: My trouble is how the classifier $G_m(x)$ is trained, What does mean a classifier to be trained using weights $w_i$? Is it to fit classifier through $\{w_i,y_i\}_{i=1}^{N}$?...
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1answer
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How to test whether data is clustered wrt. subcategories?

I have a dataset of about 2000 entries, containing two numerical values, one categorical and one sub-categorical label for each entry. The data is from chemistry lab data, but for the purpose of this ...
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Q: Training a CNN-LSTM on video inputs

Hello everyone! I implemented the following model, for action classification from videos, where each frame is 224x224x3, a video consists of ...
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oversampling multivariate time series data

For some classification needs. I have multivariate time series data composed from 4 stelite images in form of (145521 pixels, 4 dates, 2 bands) I made a classification with tempCNN to classify the ...
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1answer
25 views

Robustness vs Generalization

I don't quite understand the difference between robustness and generalisability in relation to image processing (CNN). If my model generalises well, it is also robust to changes in the image material. ...
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Dealing with unbalanced training set compared with real world data

I am in charge of a fraud detection model that prevents fraudulent users from using our solution. My model is performing great but the issue I have is that the more the model becomes performant the ...
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1answer
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Two-level (large category and small category) label classification problem

At present, there is an app classification task, the input is the function description of the app, and the two labels are the major category to which the app belongs and the small categories under the ...
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Implementing Smoothed Isotonic Regression

In the paper here the authors suggest a new way of calibrating classifiers, called Smoothed Isotonic Regression (Algorithm 1). As I follow the algorithm along, I noticed a problem in lines 19-20: ...
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What is the python code to find G-mean of classification models? [closed]

I am working on evaluation classification models for multiclass imbalanced problem.
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Dividing a data set into segments with consistent inner behavior, using segmentation algorithms and metrics for consistency

Context of the problem: I have signal data which was recorded in a software system and which shows the runtime of multiple processes over time. In total there are more than 900 processes each having ...
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XGBoost for a binary classification where features are of different types

I have a dataset of "questions in an exam" that contains features such as: QuestionLength (float) averageTimePerQuestion (float) hasMedia (boolean, represented as 0 or 1) averageOfHardWords ...
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1answer
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How many layers of a pretrained model shoud be frozen?

I'm following an example of transfer learning where the blogger has frozen the first 20 layers of MobileNet. My question is , that is there any rule of thumb for how many layers should be frozen? ...
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1answer
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How do I calculate precision, recall, specificity, sensitivity manually?

I have actual class labels and predicted class labels: ...
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1answer
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Is there any difference between classifying images by their type and by the objects they represent?

Let us suppose that I would like to train a machine learning model for classifying images according to their types (for example, photographs and drawings). The techniques that I can use for this would ...
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2answers
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What is the difference between LabelBinarizer and MultiLabelBinarizer?

I am trying to understand the difference between the two label encoding techniques for output variable. I have read things but still can't get a clear picture as what makes them different. Also can we ...
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2answers
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Need help to increase classification accuracy for classified ads posting

I have to predict the category under which ad was posted using the provided data; I cannot gain accuracy more than 74% for my model. I am not sure what I am missing. What I have done so far: Cleaned ...
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1answer
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When should we use jaccard score?

I am a newbie in Machine Learning, I trained a binary classifier for bank loan prediction through Logistic Regression. I measured the accuracy of it with two methods: accuracy score and jaccard index. ...
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How to deal with Different Shapes of X_train and X_test after OneHotEncoding?

I am trying to perform OneHotEncoding as well as feature scaling on my training and testing data separately, steps I did: ...
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1answer
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How to visualize data after performing OneHotEncoding and normalization?

I have a dataset and on that, I have performed OneHotEncoding and Standardization using standard scalar, Now that I have preprocessed data I have to visualize it, but on converting it to pandas ...
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How to perform feature selection on a dataset using correlation-based feature selection process

I have a dataset and on that, I have to perform feature selection using a correlation-based feature selection process (using scikit-learn), can anyone please show me how to do it with a small example ...
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2answers
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When Does Feature Selection Takes Place?

I have a dataset where there are categorical features as well as numeric features, and I have to perform OneHotEncoding, Normalization and feature selection on it. In what order should I perform these ...
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1answer
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Keras: Custom output layer for multiple multi-class classifications

Hello, I’m quite new to machine learning and I want to build my first custom layer in Keras, using Python. I want to use a dataset of 103 dimensions to do classification task. The last fully connected ...
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How do I deal with unbalance classes in a stock market prediction problem?

I am working on a prediction model to predict whether a stock should sell, hold or buy in n days. Each day (or row in the dataset), I classify whether this should ...
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3answers
35 views

How are scores calculated for each class of binary classification

The formula for Precision is TP / TP + FP, but how to apply it individually for each class of a binary classification problem, For example here the precision, recall and f1 scores are calculated for ...
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1answer
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scikit-learn OneHot returns tuples and not a vectors

First I do a label encoding to all the columns that are strings so they will be numeric. After that, I take just the columns with the labels, convert them to np array, reshape, and convert them to one-...
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Though process to calculate error rate for a classification algorithm with 1000 objects?

I am trying to solve this question A classification algorithm classifies 1000 objects in to one of two classes. It incorrectly classifies 13 out of 100 class 1 objects and 53 class 2 objects. (a) What ...
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1answer
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Understanding SVM's Lagrangian dual optimization problem

I was going through SVM section of Stanford CS229 course notes by Andrew Ng. On page 18 and 19, he explains Lagrangian and its dual: He first defines the generalized primal optimization problem: $$ \...
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Best Practices For Dealing With This Scenario

I'm presently building a spam classifier. The model is unable to even overfit the training set at present. To investigate, I plotted the distributions of the model's features, and compared them across ...
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1answer
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Understanding Lagrangian equation for SVM

I was trying to understand Lagrangian from SVM section of Andrew Ng's Stanford CS229 course notes. On page 17 and 18, he says: Given the problem $$\begin{align} min_w & \quad f(w) \\ s.t. &...
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1answer
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Loss in multi-class classification

I have a multi-class classification task. One of the standard approach in choosing loss function is to use a CrossEntropyLoss. It is a good option when classes are standonlone and not similar to each ...
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3answers
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How to get the correct confusion matrix in imbalance class dataset?

I have created two simulated random dataset of 3 classes. Only difference between the dataset is that frequency of the classes. ...
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1answer
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Binary document classification using keywords for a very small dataset

I have a set of 150 documents with their assigned binary class. I also have 1000 unlabeled documents. Each document is about the length of a journal paper. Each class has 15 associated keywords. I ...
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ML algorithms recommand of online/batch learning for classification, prediction( and targetfunction), dataset parameter and label (A, B, C, Label)

Currently i am in a project. I will receive processing data constantly online from CNC machine, which will be like a dataset with parameters and labels, for example [A,B,C,Label],like 1st picture. The ...
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2answers
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Convolutional Neural Network for Signal Modulation Classification

I recently posted another question and this question is the evolution of that one. By the way I will resume all the problem below, like if the previous question didn't ever exist. Problem description ...
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Question about text classification without labeled data

I am working on a text classifier but at the moment I'm quite lost on what to do. The classes form a tree with three levels, for example, class A (level 1), class A.1 (level 2, subclass of A), and ...
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1answer
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Visualizing the equation for separating hyperplane

I was wondering if I can visualize with the example the fact that for all points $x$ on the separating hyperplane, the following equation holds true: $$w^T.x+w_0=0\quad\quad\quad \text{... equation (1)...
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TSNE parameters

Trying to tune the parameters of sklearn.manifold.TSNE(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate=200.0, n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, ...
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1answer
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How to extract features insights to change classifier decision?

I don't know if my question is specific enough but there's what I mean. Suppose we have high school grades of students who attended a Computer Science degree and whether or not they succeeded (given a ...
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2answers
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How much data augmentation is required on an imbalanced dataset?

Imagine I have a dataset with positive and negative sentences, and I need to train a transformer (Like BERT) to do the binary classification. The problem is that there are 100 negative sentences and ...
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1answer
51 views

Understanding Lagrangian for SVM

I was referring SVM section of Andrew Ng's course notes for Stanford CS229 Machine Learning course. On page 22, he says: Lagrangian for optimization problem: $$\mathcal{L}(w,b,\alpha)=\frac{1}{2}\...

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