Questions tagged [binary-classification]

For questions about binary classification problems, i.e. problems where we want to classify inputs into 1 of 2 classes.

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Binary Classification Model Comparison - Interpretation of Training, Test and Validation Set Performance

I am looking for some advice regarding the best choice of binary classification model based on training, validation and test set results. Model 1 (results in 1st image) shows better test set results ...
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Why do smaller weights converge faster for RNNs?

I am writing a Recurrent Neural Network using only the NumPy library for a binary classification problem. When I initialize the weights with np.random.randn, after 1000 epochs it gets ~60% accuracy, ...
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ML algorithm suggestion for databases that change a lot with time after model training

I have a classification problem and I'm using a logistic regression (I tested it among other models and this one was the best). I look for information from game sites and test if a user has the ...
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What architecture would be best to match images of torn pieces of tapes?

I am currently working on a project where the goal is to create a neural network that can determine if two pieces of torn tapes are a true fit or not. My current idea is a convolutional network that ...
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Prediction of continuous variable based on threshold

The independent variables are date, count, atmp, and ...
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How can my RNN get way better results than my ANN [closed]

So, I'm using the same dataset in both models but my RNN gets a 95% accuracy and my ANN gets 52%. It is a time series, binary classification problem, and I know that RNN is better than ANN for time ...
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Does Using the Same Background for Binary Classification Improve Model Accuracy?

I am training a CNN that detects if a there is a pot of boiling water vs if there is a pot of boiling water with pasta inside. My hypothesis is that having the same background for both a positive and ...
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52 views

Is categorical cross entropy better than binary cross entropy for imbalanced binary classification problems

I am training a NN model. The data is highly imbalanced (3% for positive labels), and I have not resampled more true classes in the training set. The model performs much better when categorical cross-...
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Which pre-processing steps are necessary for Deep Learning models to solve a document classification problem?

I have created a data set with 30.000 text documents (each text file is rather small with respect to its length), which are labelled with 0 and 1. Using this data set, I want to train machine learning ...
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Comparing large numbers of images to find outliers

There are many methods you can use to compare two images in ML (Siamese NN, CNNs, Ect.) What I cannot figure out is comparing a large number of images (Without Retraining) to find images of a ...
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Given a dataset of people with and without cancer, should I split it into training and test datasets such that the same person is not in both?

I have a database that contains healthy persons and lung cancer patients. I need to design a deep neural network for the binary classification problem (cancer/no cancer). I need to split the dataset ...
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How to arrange test dataset distribution for an imbalanced classification problem?

I have a dataset that contains 560 datapoints, and I would like to do binary classification on it. 400 datapoints belong to class 1, and 160 points belong to class 2. In the case of an imbalanced ...
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Is there a way to update the neural network to fit the new data without the time required for retraining?

I built a basic neural network in MATLAB. The neural network classifies points on the X-Y axis system into two classes (0 and 1). (I try to get the function that represents a shape from this photo) ...
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Explainable AI for complex input features

I have a model for binary classification that includes 2 linear layers with RELU activation function and Sigmoid in the last layer. The input features are FastText word embedding, frequency, and ...
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Is a test accuracy of 0.74 good enough, given a dataset of about 700 samples, and, if not, how can I improve it?

I am new to neural networks. I am trying to solve a binary classification problem. Specifically, I want to determine whether a patient has or not a certain disease based on the dataset. The dataset ...
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Can I perform 3D point cloud per-point labeling from binary classification alone?

All, It seems that the process of individually labeling points in 3D point clouds is no small task. I believe that's why tools like these exist: Sagemaker Pointly But ... what if there are only two ...
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Is it normal that we get different AUC results after running with various seeds?

We are working on optimizing a CNN made for binary image classification (by that I mean to classify each image to group A or group B). It is based on InceptionV3, using PyTorch. We saw that choosing ...
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How can I weight each point in one-class SVM?

I want to give weights to some data points Specifically, these are points related to anomalies (I'm implementing one-class SVM for anomaly detection) Exactly, I want to consider some data points that ...
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In logistic regression, why is the binary cross-entropy loss function convex?

I am studying logistic regression for binary classification. The loss function used is cross-entropy. For a given input $x$, if our model outputs $\hat{y}$ instead of $y$, the loss is given by $$\text{...
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What are pros and cons of using a multi-head neural network versus a single neural network for multi-label classification?

I haven't been able to find a good discussion specifically comparing the two (only one describing a classification and regression problem). I am training a classifier to learn both age and gender ...
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Is my dataset unlearnable, or is my LSTM model not smart enough?

I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp: The distribution over the first 30 seconds is as follows: I have ...
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How to train a machine learning model with multiple attributes and one target value?

I'm working on a machine learning problem where I need to guess which customers will churn and which of them will continue to be customers. I have $X_0, X_1, X_2, X_3, X_4, X_5$ and $X_6$ attributes ...
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123 views

Is binary classification using CNN possible if the training data only consists of one class?

Is binary classification using CNN possible if the training data only consists of one class? I am working on landslide risk assessment using Convolutional Neural Networks and I want to train a network ...
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2 votes
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225 views

How to get more accuracy of the logistic regression model?

I am working on a Baby Crying Detection model using logistic regression. Out of $581$ audios, $222$ are of a baby crying. Each audio is of $5$ seconds. what I have done is convert each audio into ...
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Can RNNs be used to classify these time series into two classes?

My task is to classify into two classes the time series like these shown in the figure. The figure shows one class on the left sub-figure and second one on the right. The series are shown in pairs ...
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Could I just choose the other (non-predicted) class when the accuracy is low?

I have a binary classification problem. My neural network is getting between 10% and 45% accuracy on the validation set and 80% on the training set. Now, if I have a 10% accuracy and I just take the ...
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1 answer
1k views

Is it appropriate to use a softmax activation with a categorical crossentropy loss?

I have a binary classification problem where I have 2 classes. A sample is either class 1 or class 2 - For simplicity, lets say they are exclusive from one another so it is definitely one or the other....
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1 answer
81 views

Which approach should I use to classify points above and below a sine function $y(x) = A + B \sin(Cx)$?

In a linear regression problem, a line can divide a data set into two categories. So, basically, points above the line belong to category 1, and points below the ...
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Why are CNN binary classifier output probability distributions often skewed?

I've been working on a lot of simple resnet18 binary classifiers lately and I've started to notice that the probability distributions are often skewed one way or the other. This figure shows one such ...
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2 votes
1 answer
3k views

What is the definition of the hinge loss function?

I came across the hinge loss function for training a neural network model, but I did not know the analytical form for the same. I can write the mean squared error loss function (which is more often ...
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Can you use machine learning for data with binary outcomes?

I am totally new to artificial intelligence and neural networks and have a broad question that I hope is appropriate to ask here. I am an ecologist working in animal movement and I want to use AI to ...
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Semantic segmentation failing in small instance detection

I performed semantic segmentation with U-net. My dataset consists of grayscale images of defects. After training the dataset for I got an metric accuracy of only 0.3 - 0.4 IOU. Eventhough it is merely ...
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1 answer
394 views

How should we interpret this figure that relates the perceptron criterion and the hinge loss?

I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following: The perceptron criterion is ...
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2 votes
1 answer
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Why doesn't the set $\{ -2, +2 \}$ in $E(X) = (y − \text{sign}\{\overline{W} \cdot \overline{X} \}) \in \{ −2, +2 \}$ include $0$?

I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following: The perceptron criterion is ...
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Should binary feature be in one or two columns in deep neural networks?

Let's assume I have a simple feedforward neural network whose input contains binary 0/1 features and output is also binary two classes. Is it better, worse, or maybe totally indifferent, for every ...
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Support Vector Machine Convert optimisation problem from argmax to argmin

I'm new to the AI Stackexchange and wasn't certain if this should go here or to Maths instead but thought the context with ML may be useful to understand my problem. I hope posting this question here ...
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How to use residual learning applied to fully connected networks?

Is there any reason why skip connections would not provide the same benefits to fully connected layers as it does for convolutional? I've read the ResNet paper and it says that the applications should ...
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Image Classification for watermarks with poor results

Just starting learning things about tensorflow and NN. As an exercise I decided to create a dataset of images, watermarked and not, in order to binary classify these. First of all, the dataset ( you ...
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2 votes
1 answer
457 views

Why am I getting a difference between training accuracy and accuracy calculated with Keras' predict_classes on a subset of the training data?

I'm trying to solve a binary classification problem with AlexNet. I split the original dataset into training and validation datasets using a 70/30 ratio. I have trained my neural network with a ...
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4 votes
2 answers
108 views

Does summing up word vectors destroy their meaning?

For example, I have a paragraph that I want to classify in a binary manner. But because the inputs have to have a fixed length, I need to ensure that every paragraph is represented by a uniform ...
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2 votes
1 answer
112 views

How to perform binary classification when one class is more predominant than the other?

Assuming we have big $m \times n$ input dataset, with $m \times 1$ output vector. It's a classification problem with only two possible values: either $1$ or $0$. Now, the problem is that almost all ...
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When doing binary classification with neural networks, how can I order the importance of the features for a class?

I have a simple neural network for binary classification. The input features include age, sex, economic situation, illness, disability, etc. The output is simply 1 and 0. I would like to order the ...
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2 votes
1 answer
884 views

Which loss function should I use for binary classification?

I plan to create a neural network using Python, Keras, and TensorFlow. All the tutorials I have seen so far are concerned with image recognition. However, the goal of my program would be to take in 10+...
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1 answer
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Are there any advantages of using rules-based approaches versus models for detecting spam?

Suppose that we have unlabeled data. That is, all we have are a collection of emails and want to determine whether any of them is spam or not. Let's say we have $1,000$ rules to determine whether a ...
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1 answer
60 views

How many parameter would there be in a logistic regression model used to classify reviews into "good" or "bad"?

Suppose we want to classify a review as good ($1$) or bad ($0$). We have a training data set of $10,000$ reviews. Also, suppose we have a vocabulary of $100,000$ words $w_1, \dots, w_{100,000}$. So ...
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2 votes
1 answer
130 views

How can I use Generative Adversarial Networks to solve the imbalanced class problem?

Problem setting We have to do a binary classification of data given a training dataset $D$, where most items belong to class $A$ and some items belong to class $B$, so the classes are heavily ...
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1 vote
1 answer
136 views

Why is my fine-tuned YOLO model detecting other objects as a human?

I am new to deep learning and computer vision. I have a problem where I use the YOLO to detect objects. For my problem, I just want to recognize 1 human only. So, I changed the final YOLO's layer (...
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3 votes
3 answers
221 views

If we want to classify something as either a cat/dog or neither, do we need 2 or 3 classes?

Suppose one trains a CNN to determine if something was either a cat/dog or neither (2 classes), would it be a good idea to assign all cats and dogs to one class and everything else to another? Or ...
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