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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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Flipping train and test labels for binary classification

I was training a GCN (this one) on a single graph (n=1,1304 nodes, num_features=26) to perform node level binary classification. However, my model performed with 5% accuracy (and even went as low to 0%...
Heisen _'s user avatar
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1 answer
25 views

How to incorporate the probability threshold for binary classification into scikit-learn GridSearchCV? [closed]

How would I perform grid search in scikit-learn including over the probability threshold for binary classification? In my search, most answers suggest first fitting a model and then performing a loop ...
bonzo_pippinpaddle's user avatar
2 votes
1 answer
60 views

If the output is 0.09, does this mean that the prediction is class 1 or 0?

I use a Keras EfficientNetB7 and transfer learning to solve a binary classification problem. I use tf.keras.layers.Dense(1, activation="sigmoid")(x) for ...
Doug's user avatar
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1 vote
1 answer
124 views

Does the root node of a Decision Tree change when one data point is removed?

I recently took an online quiz on Machine Learning. One question was particularly confusing to me. The question is stated below. Consider a dataset Z on which a decision tree is built. Consider the ...
Bigyan Suryabanshee's user avatar
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12 views

Does the order of iteration affect the answer returned by FIND-S?

This paragraph is from the book Machine Learning by Tom M.Mitchell (Page 26): Initialize $h$ to the most specific hypothesis in $H$ For each positive training instance $x$ $\;\;\;\;\;\;$.For each ...
Emad's user avatar
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0 answers
36 views

Approaches for multi-label classification with over 1,000,000 labels

I have billions of rows in some dataset and each row can be in any subset of about 1 million binary labels. So the number of overall classes would be $\sim 2^{1,000,000}$, if I were to think about it ...
economicagent's user avatar
0 votes
1 answer
125 views

Image segmentation with varying resolution

I am looking to create a model that is able to perform binary segmentation of images with varying resolutions. For model should be able to classify tree or not tree regardless of the resolution of the ...
cmosig's user avatar
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40 views

Any research in "probe-tuning" of LLMs?

Is there any research in "probe-tuning" of LLMs, i.e., tuning LLM's parameter weights such that a specific probe (classifier) is more reliably detecting certain markers throughout the ...
leventov's user avatar
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1 answer
51 views

Computer vision algorithms for binary classification of bird images

I want to start a project to detect if an image is a crow or not a crow (crow as in the black bird). Is this referred to as "binary classification?" If I wanted to use open source Python ...
Alex Totheroh's user avatar
1 vote
0 answers
28 views

What are the most common fault prediction algorithms?

I have to predict a fault (automotive related) as much in advance as possible. Right now I have found a solution that is somewhat satisfactory (a good number of true positives and a low number of ...
Pigna's user avatar
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43 views

Not pre-trained binary transformer model

I stacked with a problem. My default Transformer model totally does not learn how to evaluate python logical expressions, like: '(False and not True) xor False or (not False and False)'. Model should ...
Oleksandr Ovcharenko's user avatar
3 votes
0 answers
55 views

Why does a neural network struggle to solve this simple problem?

Consider the following problem: Given a vector x of size dim with values between 0 and 1 (exclusive), determine if ...
Daniel's user avatar
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-1 votes
1 answer
150 views

I’m making a simple neural network from scratch and it won’t learn anything. Please help [closed]

I am coding a classifier neural network from scratch. It is not really learning and I believe that somewhere there is a gradient explosion/vanishing issue. Could be some other stuff as well that I ...
Maks's user avatar
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0 votes
1 answer
265 views

Why does KNN Model return 99% accuracy on dataset with default parameters? [closed]

I am building a model that predicts if a user will like a stock or not based on different features, such as Market Cap, Current Ratio, Sector, Trailing PE, etc. I am going to implement this model in a ...
Messi10's user avatar
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1 answer
59 views

How to evaluate binary classifier on imbalanced dataset?

I have trained a Decision Tree model on an imbalanced dataset. I got the following results for the test set from the sklearn and imblearn classification reports (attached below). Moreover, the other ...
Zal's user avatar
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1 answer
384 views

How to specify categorical features in cat boost?

I have a dataset (pandas data frame) with all features of type int32 containing continuous values except one feature state_number, its data type is int32, but it ...
Zal's user avatar
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1 answer
152 views

How to interpret binary classification metrics on an imbalanced data set?

I have an imbalanced dataset on intrusion detection. I have (attack class) 3668045 samples and (benign class) 477 samples. I made a 70:30 Train test split. My problem is to predict whether the given ...
Zal's user avatar
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1 vote
1 answer
116 views

machine learning for a budgeting application

I am interested in finding references and previous applications where prior year budgets are analyzed to provide guidance for a current year budget. Specifically, each year some two thousand items ...
rbmales's user avatar
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1 answer
143 views

How does Supervised learning models handle time-varying data

I need to train a supervised learning model which would take some input which differs in its output relating to time. to better understand my question I would give a simple binary classification, the ...
L.Adham's user avatar
1 vote
0 answers
267 views

Categorical loss function for variable number of labels

I have a model for binary classification. The target variable has the different number of labels (instances) in each sample. For example, a batch of size 2 with 2 and 3 instances and correspondingly ...
Mykola Zotko's user avatar
0 votes
1 answer
103 views

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 ...
Rowan Barua's user avatar
0 votes
1 answer
22 views

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, ...
user avatar
1 vote
1 answer
42 views

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 ...
Marcos Almeida's user avatar
1 vote
1 answer
47 views

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 ...
sockthehawk107's user avatar
1 vote
1 answer
89 views

Prediction of continuous variable based on threshold

The independent variables are date, count, atmp, and ...
There's user avatar
  • 111
0 votes
1 answer
42 views

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 ...
Miguel Casagrande's user avatar
1 vote
1 answer
87 views

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 ...
Joel Castro's user avatar
1 vote
1 answer
283 views

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 ...
MiFischer22's user avatar
0 votes
1 answer
68 views

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 ...
Noha's user avatar
  • 111
0 votes
1 answer
231 views

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 ...
Clara's user avatar
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3 votes
1 answer
163 views

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) ...
shlomo odem's user avatar
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0 answers
64 views

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 ...
Abolfazl Mohajeri's user avatar
0 votes
0 answers
406 views

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 ...
FateMintTrio's user avatar
0 votes
0 answers
81 views

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 ...
Dae-Young Park's user avatar
5 votes
3 answers
5k views

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{...
hanugm's user avatar
  • 3,890
1 vote
1 answer
732 views

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 ...
user9317212's user avatar
1 vote
0 answers
23 views

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 ...
bbasaran's user avatar
  • 133
1 vote
0 answers
287 views

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 ...
imageprocessingproblem's user avatar
0 votes
3 answers
199 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 ...
Rmin's user avatar
  • 1
2 votes
1 answer
736 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 ...
Muhammad Waqar Anwar's user avatar
0 votes
0 answers
38 views

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 ...
Marek's user avatar
  • 9
0 votes
2 answers
94 views

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 ...
jr123456jr987654321's user avatar
1 vote
1 answer
4k 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....
user9317212's user avatar
1 vote
1 answer
128 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 ...
Quadro's user avatar
  • 111
1 vote
1 answer
398 views

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 ...
Alexander Soare's user avatar
2 votes
1 answer
6k 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 ...
hanugm's user avatar
  • 3,890
0 votes
1 answer
644 views

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 ...
mb5572's user avatar
  • 3
0 votes
1 answer
176 views

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 ...
shankar ram's user avatar
7 votes
2 answers
895 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 ...
The Pointer's user avatar
2 votes
1 answer
130 views

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 ...
The Pointer's user avatar