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Questions tagged [binary-classification]

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How to tune the classification threshold in a cost-sensitive manner?

I have trained a classifier outputting probabilities for each class. I want to tune the decision threshold in such a way that it accounts for different costs/gains assigned to false positives ($FP$), $...
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-1 votes
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Difficulty learning binary classification for 'very similar' inputs

I have been struggling to train a classifier to tell the difference between 'bad' and 'good' datapoints for a physics application. Essentially I am inputting as features some parameters that are used ...
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1 answer
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How do I compute and plot Bias and Variance of a classifier in Python?

I'm new to Machine Learning and I understand bias and variance in theory but I can't seem to find a single source that explains how bias or variance can be computed. I'd like to do it in Python and ...
William's user avatar
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Why PySpark `BinaryClasssificationEvaluator` metric `areaUnderROC` returns slightly different across multiple evaluations on the same dataset?

I am using BinaryClasssificationEvaluator in Pyspark to calculate AUC, however, I find that the returned auc across multiple evaluations on the same dataset are ...
helloworld's user avatar
1 vote
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37 views

Class imbalance for binary classification tasks

I am looking to train a binary classifier. Most of my experience so far has been with generative models, not classifiers, so I am wondering with respect to training data, what is a good ratio of 0 and ...
Wigeon's user avatar
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Interpreting the SHAP values presented in layered violin plot in SHAP-library for Scikit binary RandomForestClassifier

I am using the SHAP-library for computing feature Shapley values for a binary RandomForestClassifier which has naturally two outputs, 0 or 1. The forest itself consists from 100 decision tree ...
jjepsuomi's user avatar
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10 views

Remove feature from kernel matrix

I am pretty new to machine learning so please bear with me :) I am trying to do a binary classification task using an SVM with precomuted kernel (in python using sklearn). I created my train kernel ...
Georgia's user avatar
1 vote
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About GANs specialization

I'm working in a binary classification problem. In general terms, I'm focused on using neural networks to classify datasets that are not huge and in order to address that problem I'm comparing ...
leapofFaith's user avatar
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Getting nearly 100% accuracy using Binary Classification in Tensorflow but incredibly wrong prediction levels for email messages

I'm creating a Chrome Extension to read user emails via Gmail's API, and then passing in user emails to a trained Keras model in Flask to determine whether the email was written by an AI or a Human, ...
Chibuike S. Eze's user avatar
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18 views

How can i retrieve incorrect labelled predictions on new unseen data on image classification when i want to retrain my model later on?

I've recently made a binary image classification model with transfer learning. The model is used on an api and the predictions gets saved into a database. The problem is that it predicts images ...
Enes Aygun's user avatar
2 votes
1 answer
66 views

How does dependency information impact binary classification in multi-label prediction models?

TL;DR: I don't understand the dependency issue with binary classification (binary relevance) compared to multi-label prediction models. I often read in papers that some kind of "dependency ...
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Multilabel Classification - Flat Binary Classifiers vs Hierarchical Binary Classifiers

Was researching on multi label classification to solve the problem of tagging news articles with topics and countries, where tags follow the syntax <topic>-<country>, and would like to ...
curious-24-7's user avatar
1 vote
0 answers
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When can we claim that the training converged?

I've been working for a while in a binary classification problem with different types of neural networks. In this particular case, I'm using an 3-layer MLP with hyperbolic tangent activation in input ...
leapofFaith's user avatar
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0 answers
11 views

How does hyperparameter tuning work for constructing/choosing a final model using Nested Cross validation?

I want to determine if XGBoost is better than random forest or logistic regression for building a binary classification model. The model will be a composite model, with a feature selection model to ...
reuben george's user avatar
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Computing empirical coverage in binary classification confidence estimation

I am trying to compute confidence interval empirical coverage. I have computed the confidence intervals for binary probability predictions. For example, the model estimates that there is probability ...
Tiago Melo's user avatar
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20 views

Are my CNN loss and performance curves valid, or are they showing under or overfitting?

Thanks in advance for any help offered. I am using a Keras CNN to perform binary classification (credit card transactions fraud vs non-fraud). Below is my results for 100 epochs. It feels odd that the ...
luckylogic's user avatar
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29 views

While working on binary image classification, the class mode set to binary incorrectly labels the images, but does it correct on categorical

I am currently working on a binary image classification. My problem is that when i use data augmentation, it incorrectly labels the images when it is set to binary. The things i have tried: Looked ...
Enes Aygun's user avatar
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Can someone interpret my Binary Cross Entropy Loss Curve?

I am trying to understand my loss curve using : tf.keras.losses.BinaryCrossentropy() Question 1: Based on my loss curve/accuracy, would it be wise to proceed to feed it into a ensemble learning model ...
Leibon Jarbis's user avatar
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24 views

Binary classification penalizing only one class

Let's suppose that the task is the following binary classification: Does this image have a cat or a dog? These are the four possible predictions: It predicts cat and the image has cat. It predicts ...
Konstantinos's user avatar
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28 views

Gradient boosting algorithm implemented in LightGBM

I'm currently reading the documentation of LightGBM and I'm wondering which gradient boosting algorithm is exactly implemented there if choose boosting parameter as "gbdt" or "dart?&...
Julian's user avatar
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4 votes
1 answer
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Does a random classifier have a diagonal ROC (received operator characteristic) curve even when the data is biased toward negatives?

About 9% of the US population have a diabetes diagnosis. So a binary random classifier that just guesses 50% positive and 50% negative would likely be incorrect when it guesses positive (leading to ...
joseville's user avatar
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Pycaret cross validation scores are way lower than unseen test set scores

How come the cross-validation model scores are much lower than the model scores on an unseen dataset? ...
yoavf's user avatar
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1 vote
1 answer
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How to balance labeled datas and then carry out execution with a certain ratio?

I'm building a binary classification model using a neural network, with python and the libraries tensorflow and keras. For that I have an unequal amount of labeled data: Around 2'000'000 labeled with <...
user155518's user avatar
3 votes
2 answers
371 views

ROC curve for a perfect model, why is AUC 1.0?

According to this article, The ROC curve for a perfect model would go straight up the TPR axis on the left and then across the FPR axis at the top. Since the plot area for the curve measures 1x1, the ...
T. Webster's user avatar
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0 answers
124 views

Empty Confusion Matrix and Zero Precision/F-score

Could you please say why I'm getting this warning while doing a binary classification using Artificial Neural Networks? The data are colored images. ...
Totoro's user avatar
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1 vote
1 answer
80 views

Correct way to take a subset of a dataset?

I am attempting a binary classification problem (using Weka). My dataset has 100,000 rows, 14 attributes (1 output variable). It takes already too long just to open the dataset in excel so I just know ...
FlexMcMurphy's user avatar
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0 answers
31 views

Model can not fit to 8 datapoints

I have 10 groups of biological experiments, all of size 100. I want to estimate experimental performance (success rate) of each groups of experiments, but have only ran experiments in two groups. My ...
TheNumber23's user avatar
1 vote
1 answer
78 views

How do I best approach a multiple-target binary classification in Tensorflow/Keras?

I currently have eight features which are either categorical or continuous variables. My targets are many (~1000) binary variables. So far I have attempted skmultilearn and sklearn.multioutput. I ...
FoolsGold1997's user avatar
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0 answers
16 views

Why is my Histogram Gradient Boosting Classifier model still producing type II error? How can I reduce the type II error?

Type 2 error and how to hypertune or feature engineer a solution for it I trial and tested different techniques and kept the structure which made the most sense to me. But still my model confusion ...
bitebytebit's user avatar
0 votes
2 answers
47 views

Binary classification using RNN not going beyond 50% accuracy

I am trying to find out the reason behind why my RNN network won't go beyond 50% for binary classification. My input data is of the shape: ...
Prabhjot Singh Rai's user avatar
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45 views

Competition test set performance much lower than validation set

We are a team of 3 participating in a university competition for a deep learning course. The competition involves a binary image classification task where we have to predict leaf diseases on a (5200, ...
Fiorenzo Fiorenzi's user avatar
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30 views

Logistic regression with E-net regularization produces different set of weights with each run

I am currently trying to make a model to classify brain tumor patients by incidence of epilepsy using a combination of variables extracted from clinical records, and radiomics features from segmented ...
reuben george's user avatar
0 votes
1 answer
319 views

Training ResNet50 model for binary classification

I want to use ResNet50 model to perform binary classification on a dataset spectrogram dataset. In order to do that I had to make a couple of modifications to the model's architecture: Modified the ...
leapofFaith's user avatar
0 votes
0 answers
26 views

Binary classification using xgboost

Why when adding new features in my ADS for a binary classification using XGBOOST my score and uplift has decreased ? What is the best way to treate categorical features or other features in order that ...
Warda_IDRIS's user avatar
0 votes
1 answer
135 views

Different accuracy scores with sklearn roc_auc_score on same model using sklearn.metrics

Why do these below lines give different outputs while the input is the same? I need to report these results in paper, but I am unsure which is better and why. ...
Adnan Ali's user avatar
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3 answers
103 views

Reduce false positives having imbalanced data

I'm using a DNN-48 having the following scenario: Features: 8 (48 at the end because I generate conditional sequences of 6 elements each) Classes: Y=0 (90%), Y=1 (10%) Precision and recall are good ...
Gabriel's user avatar
0 votes
0 answers
24 views

Decision making in a binary classification problem

Consider a two-dimensional feature space in which the line $\mathbf{w}.\mathbf{x} + b = 0$, where $ \mathbf{w},\mathbf{x} \in \mathbb{R}^ 2 $ and $b \in \mathbb{R}$, separates linearly separable data ...
Tirthankar's user avatar
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0 answers
16 views

Predicting Year-End Outcome from Monthly (and Annual) Data

I have data on customers' usage of various product features over time. Each month, a customer can choose to use a feature or not. I want to create a live system that produces the probability of a user ...
pyassign67's user avatar
2 votes
1 answer
50 views

Changing model architecture doesn't impact results

I am currently learning binary classification. The problem is classifying positive and negative movie reviews. The dataset is 25,000 reviews with each review represented by 10,000 of the most used ...
Omer Mualem's user avatar
0 votes
2 answers
93 views

Can you obtain classification thresholds for specific features in a Random Forest?

Say I've created a random forest model for binary-classification prediction target of either "Pass" or "Fail" for a group of students based upon numerical features "Hours ...
Petunia's user avatar
0 votes
2 answers
107 views

Is this an unusual distribution for a sigmoid output from a neural network?

Shown here is the histogram of around 130K predictions of my deep neural network that is classifying some financial data. This is on the dev set but a similar distribution is also seen on the train ...
BYZZav's user avatar
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0 answers
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Binary classification metrics for one-hot label encoding in Tensorflow

I run a binary classification using different CNN versions in Tensorflow. When I label samples from each class using 0 and 1, I select a sigmoid output in the last layer of the CNN, like ...
GKH's user avatar
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0 votes
0 answers
109 views

BCE loss stuck at 0.693 in the beginnng of training and then started to decrease, why?

I'm using a Transformer encoder with a binary cross entropy loss for CTR prediction. The training batch loss is at around 0.693 constantly for the beginning several thousand steps (batches). I'm using ...
CyberPlayerOne's user avatar
0 votes
1 answer
588 views

Torchmetrics Binary Accuracy and Multiclass Accuracy don't match

in my program I have the problem that for a 2-class classification problem my multiclass accuracy and binary class accuracy don't match. I have generated a very small sample example where you can see ...
YumYum's user avatar
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0 votes
1 answer
12 views

Classification Problematics : Feature Number Variance & Feature Repetition

I have a harsh case study (in my mind). The problem is I need make binary classification on Quality of Service (good or bad). I have a feedback on quality on groups of devices belonging to company. I ...
secuf's user avatar
  • 1
1 vote
1 answer
594 views

precision and recall is zero

Why my model shows metrics like this? While my model was training recall and precision was equal to zero? I trying to do binary classification of mushrooms [edible, poisonous]. I have CNN model with ...
User Name's user avatar
0 votes
3 answers
243 views

How to properly do a k-fold cross validation?

I am trying to solve binary classification problem using deep neural networks. I want to compare different approaches (model architectures) and I have no hyperparameters which I want to tune. So my ...
dmasny's user avatar
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0 votes
1 answer
595 views

How to reduce the false positives to improve the models performance?

I am currently building a binary classification model to predict order return rates. I used the GradientBoostingClassifier for training the model and also performed hyperparameter tuning using ...
Kedharnath Kb's user avatar
0 votes
1 answer
89 views

Single model or multiple models for predicting at each level in a multi-level classification problem

Given a flat structured data with features that can be considered hierarchical, where each feature is at a different level (e.g., Brand at the top level, Product, Color, and Size at different levels), ...
Kedharnath Kb's user avatar
1 vote
0 answers
21 views

How to calculate threshold values for a simple binary classification model

Consider a binary classification problem with two features. Let's assume that the higher the value of each feature the more likely a datapoint is to be positively classified. Additionally assume we ...
Daniel Wyatt's user avatar