Questions tagged [binary-classification]
The binary-classification tag has no usage guidance.
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Loss drops to NaN after a short time for a time series classification
here is my model code for a binary classification of a time series:
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Text Classification misclassifying?
I am trying to solve a binary classification problem. My labels are abusive (1) and non-abusive (0).
My dataset was imbalanced (more 1 than 0s) and I used oversampling of the minority label (i.e. 1) ...
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Finding logistic loss/negative log likelihood - binary logistic regression classification
I am new to ML and data science and am struggling with a simple problem. In my problem, I am given a series of datapoints $X_i$ where $X_i = (x_{i1}, x_{i2})$ with each data point having a label $y_i$ ...
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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 ...
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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 ...
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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 ...
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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:
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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, ...
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What is the best practice to normalize/standardize imbalanced data for outlier detection or binary classification task?
I'm researching Anomaly/outlier/fraud detection, and I'm looking for the best practice to pre-process the synthetic data for imbalanced data. I have checked all methodology for normalizing/...
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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 ...
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How to deal with temporal trend in ML
I am fitting a binary classifier and I observe a temporal trend in the response variable, meaning that the actual percentage of positives fluctuates with time, I can see periods where it is high and ...
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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 ...
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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 ...
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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 ...
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Decision boundary of an neural network
Starting with a).
For the first unit: 0 * x1 + 1 * x2 + 1 > 0 (0, because the threshold is 0) which is the same as x2+1 > 0.
For the second unit: x1 * 1 + x2 * 0 + 1 > 0 (0, because the ...
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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.
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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 ...
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Aggregation of low level features for a classifier
The objective is to predict router fail/no fail (1/0) in a future time window with all the data collected over the last hour (i.e. binary target)
The data is received at two different levels:
Router ...
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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 ...
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Which classification_report metrics are appropriate to report/interpret for a binary label? Individual or macro average for both classes? scikit-learn
First, please forgive my ignorance; I am a newbie but dedicated to learning more.
Example: I have a using a random forest classifier to predict a binary outcome. The binary outcome equals 1 if people ...
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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 ...
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Is it vital to do label encoding with target variable
Should I always use label encoding while doing binary classification?
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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 ...
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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 ...
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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 ...
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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
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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 ...
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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 ...
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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 ...
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Mixed effects models for a classification task on panel data
I have a problem where I need to make predictions for a binary target $y$ given a set of features $X$ where $X$ is naturally nested in the form of repeated measurements. The data is meant to describe ...
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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 ...
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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 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 ...
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Comparing probability models for alignment
I have a probability model which predicts a probability for a binary classification problem. I am interested in how well the predicted probability aligns with the true probability. For instance, you ...
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Why does Logistic Regression perform better than machine learning models in clinical prediction studies
I am developing binary classification models to predict a medical condition in my dataset. My results show that both Logistic Regression and Linear SVM consistently outperformed other ML algorithms (...
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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), ...
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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 ...
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Weighting and loss function for multi-dimensional output on ECG neural network in Tensorflow
I am working on a DNN that is training on ecg data with a shape of [None,1,2500] and output shape of [None,12,19] where 19 is a ...
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How to bias a neural network towards one category in binary classification?
I have a basic sequential neural network built with TensorFlow.
...
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Is deep learning high initial validation accuracy a sign of problem?
I have a image classification model with 8400 images of class A and 1800 images of class B. I have used validation_split=0.2 with subsets of ...
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Understanding perceptron learning algorithm
I was revisiting perceptron learning algorithm. The wikipedia page gives the algorithm as follows:
Initialize the weights to 0 or a small random value.
For each example $j$ in our training set $D$, ...
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High dimensionality one class input
I am currently doing research on anomaly detection and currently I am facing the following problem:
My input data has only one label (anomalies) and the associated data has a very high dimension and ...
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Approaching multiple records for one observation; radiomics of 2D slices of a 3D object
Background
I am trying to create a model that can predict Type 2 diabetes in a patient based on MRI scans of their thigh muscle. Previous literature has shown that fat deposition in the muscle of ...
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How to give a column a special weight for any algorithm? (binary option)
I'm dealing with binary option, I'm making a classification model using xgboost, my idea is to predict the posterior candle color by using some data of the 20 previous candles so I made a dataset on ...
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Classifier calibration leading to worse outcome
I am trying to calibrate some classifiers to output more accurate probabilities. For this, I am using a sigmoid regression as implemented in ...
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Probability distribution of probabilities
We can get the prediction probabilities of a binary classifier from sklearn's API using the predict_proba method. Is it reasonable to expect that the shape of a histogram plotted for the prediction ...
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VC-dimension of the class of hypotheses that assign label $1$ to exactly $k$ points of some finite domain $\mathcal{X}$
Let $\mathcal{X}$ be a finite domain and $k$ a number such that $k\leq|\mathcal{X}|$. Consider the hypothesis class
$\mathcal{H}:=\big\{h:|\{\mathbf{x}\in\mathcal{X}:h(\mathbf{x})=1\}|=k\bigr\}$;
that ...
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How to get the top feature contributors for the differnt classes in the classifiction model?
In classification model , we build models with binary/multi class responses. Is there way to get the top features contributing positively,negatively to each of the classes.( i.e top features helping ...
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Binary Classification: My model classfies most data (95%+) as label 1
I am working with ECGs and trying to use a CNN model to perform binary classification. The goal is to classify 30s ECGs to detect a specific disease. I am using CNN and converting ECGs to images (...
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Using features extracted from CNN and handcrafted features to perform classification
I have a question in regards to merging features extracted from CNN and handcrafted features.
I have been reading this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002895/#B33-sensors-22-02467
...
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