Questions tagged [binary-classification]
The binary-classification tag has no usage guidance.
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Sampling Highly Imbalanced Large Dataset
I am working on a model which will run monthly on 8M users. I've snapshot-wise data in training set, eg:
Jan, 21 Snapshot : 8M Total : 233 Positives Rest Negative
Feb, 21 Snapshot : 8M Total : 599 ...
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Keras Binary Classification - Maximizing Recall
Let me start by saying my machine learning experience is... dangerous at this stage. I'm still a beginner.
I have a binary classification data set of about 100 000 records. 10% of the records are ...
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How to use confidence labels?
I have 2 sets of training data in csv files. The training data have class labels, 1 for memorable, and 0 for not memorable. In addition, there is also a confidence label for each sample. The class ...
user135529
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LGBM model predicting only single class on unseen data!
I have built a LightGBM based machine learning model on data of molecules of two classes. The distribution is as follows. Class 0 has 5933 data points and class 1 has 4696. The train test accuracy I ...
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ML model shooting up prediction probabilities
I have a scikit-learn logistic regression binary classifier and tried training it on my dataset. My model does extremely well at a threshold of 0.95 instead of 0.5 and all my predictions on example ...
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Aggregated probability based on multiple predictions on independent samples using the same classifier
i have a understanding question regarding the interpretation of a aggregation of a machine learning classifier. Lets assume i have trained a binary classifier and it was validated with a accuracy of ...
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Does eval loss decreasing slower than train loss indicate overfitting?
I am training a binary classifier using an efficientnetv2 model with a 1M image dataset where I do a 60/20/20 split. Does this graph mean that the model is over-fitting? I can see that the train loss ...
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Meaningfully compare target vs observed TPR & FPR
Suppose I have a binary classifier $f$ which acts on an input $x$. Given a threshold $t$, the predicted binary output is defined as:
$$
\widehat{y} = \begin{cases}
1, & f(x) \geq t \\
0, &...
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ROC-AUC Imbalanced Data Score Interpretation
I have a binary response variable (label) 𝐵 in a dataset with around 50,000 observations.
The training set is somewhat imbalanced with, 𝐵𝑖=1 making up about 33% of the observation's and 𝐵𝑖=0 ...
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Predictive Maintenance Question (binary classification)
I have a question regarding "Predictive Maintenance": in this tutorial here:
https://docs.microsoft.com/en-us/learn/modules/predictive-maintenance-model-builder/3-choose-scenario-data
It ...
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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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how to interpret precsion recall value in binary classification of scikit-learn
I am working with binary classification and my classification report generated through scikit-learn looks like the image below. I am confused I have two precision-recall values one for class 0 and the ...
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How do I choose the right parameters for just plain old simple standarddeviation?
I am evaluating different models that do binary classifications and basically generate trade signals. They make a prediction of either buy or sell for the next day. I look at 10 different underlying ...
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How to predict an outcome of the game (next row) based on all previous games (rows)?
I'm a data science student and I've come across a fairly unusual dataset (to me, which explains the vague title).
It's of the following form:
STAT_1
STAT_2
...
HOME
AWAY
NEXT_HOME
NEXT_AWAY
...
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When would $\Theta_{Bayes}$ be on the Equal Error Rate curve
If we use the classic Bayesian classification for a 2 class problem and classify based on comparing likelihood ratio $LR(x) = \frac{p(x|s=1)}{p(x|s=2)} $ to a $\Theta_{Bayes} = \frac{P(s=2)}{P(s=1)}$ ...
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Improving roc auc score when accuracy is good
I have got a binary classification problem with large dataset of dimensions (1155918, 55)
Also dataset is fairly balanced of 67% Class 0 , 33% Class 1.
I am getting test accuracy of 73% in test set ...
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How should I improve my CNN binary classification model from overfitting and underfitting [duplicate]
I am trying to do the cats & dogs classification problem, the problem is that my model is overfitting and I have tried all the techniques I know in order to solve but nothing is working such as ...
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Partial Dependence Plot - values range
I have trained an XGBClassifier on a dataset with binary target (0,1).
I have taken a look at the Partial Dependence Plot for each predictive characteristic.
For example:
Is it correct to assume ...
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Is there a methodology or framework for when you want to do multiple classification model runs introducing predictor variables sequentially?
I have a cohort of ~20 schoolkids and I want them to perform a sequential order of 7 tests for a competition. The scores for the tests have an average score will be around, say, 60 to 80 for each test....
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neural network binary classification softmax logsofmax and loss function
I am building a binary classification where the class I want to predict is present only <2% of times. I am using pytorch
The last layer could be logosftmax or <...
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Distilling the knowledge of a binary cross entropy with sigmoid function model to a softmax model
I have a complex CNN architecture that uses a binary cross-entropy and sigmoid function for classification. However, due to hardware restraints I would like to compress my model using knowledge ...
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Best way to represent a version feature based on percentiles
We're training a binary classifier in AutoML, and one of the features consist of browser versions.
Currently these versions are provided "normalized" to the model, according to the ...
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Give more weight to features based on distribution plot
I have a task to predict a binary variable purchase, their dataset is strongly imbalanced (10:100) and the models I have tried so far (mostly ensemble) fail. In ...
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Transformer model comparison for binary sentiment classification
On two independent datasets, I am comparing XLNet and BERT models with binary sentiment classification tasks: the Twitter dataset, where sentences are short, and the IMDB review dataset, where ...
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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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Assigning weights based on outcome probability
In a classification problem, is it suitable to assign sample weights based on their positive class probability?
For example, if I am building a binary classification problem where one of the ...
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What is the best algorithm for feature selection in categorical variables?
Imagine this question in two parts:
Part 1 : My Y variable can be only 1 and 0
Part 2 : My Y variable can be only -1, 0, 1
In both cases my X is continous with float variables. What are the ...
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Separating numerical and categorical features in a binary classification problem
I have a dataset with employee data with around 9500 rows, and have to predict if the target is 0 or 1.
Some of my features are the department of an employee, gender, salary, review_score(numerical),...
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Good model to predict color of the button to maximize site conversion
For example, we have a big internet store and we have Add to card button which can be blue or green. We want to show blue button to people who buy more with blue button and show a green button to ...
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Where shall I use odds logarithm and when shall I use sigmoid in logistic regression?
I have been interested in DS and ML recently and logistic regression was on of the first algorithms I learned. In my first course it was said that ln(p/(1-p) was used for the logistic regression. But ...
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Creating Dataset for Classification, How much balanced a good dataset should be?
I am creating a dataset with 4 classes, and there are 50K rows, I am already getting 86% accuracy, 0.85 Precison, 0.86 Recall and 0.71 F1-Score on SVM with 80,20 split.
I have to publish this dataset ...
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Repeating values caught with a binary classifier
If my machine is broken, it starts to repeat certain channels. Thing is if there are no out-liars, it is difficult to tell it's broken as we would expect all data points to be around the same value. I ...
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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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Binary classification with seperate training and testing datasets [closed]
I have two datasets (train.csv) and (test.csv) revolving around predicting the death outcome for a disease. Both sets include 20 independent variables (age, weight, etc), but only the train.csv ...
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How to customize logistic regression for this case?
I have a binary classification problem, with a dataset comprising of several features. When I train LogisticRegression on it, I get large number of false positives ...
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Handling Overfitting(diverging val loss and train loss) in Conv-LSTM
This is related to an Educational Research Project I'm using to learn stuff so please be kind.
I'm working on a video binary classification task. I have almost 1400
videos of both classes.
I also ...
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Fractional Differencing/Differentiation for Non-Time based Model; Look-ahead bias?
I have time-series data, but instead of using a time-based model like RNN, I've decided to approach my classification problem using an lgbm classifier. To do so, I have modified the data, such that ...
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Is binary classification the right choice in this case?
I am somewhat new to text classification and I have some questions if you folks can help:
I have some text I need to be able to classify as belonging to a single class or not (usually 1-10 sentences ...
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For a binary classification algorithm, is there an objective way to know how large your set of positive and negative labels need to be?
We're training a binary classification algorithm using a combined total of 2000 positive and negative labels that we purchased from a data vendor.
We mostly used all the textbook machine learning ...
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Feature importance in binary classification
I am wondering if there is a way to check the feature importance for each class in a binary classification task separately. Or any way to check the correlation between features and both target classes ...
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Using precision as a metric - how to gauge if more TP's
So precision is calculated as tp/(tp+fp)
But this doesn't seem to be a good way to assess a model as both of the below would give a result of 1?
Binary Classification
...
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Which machine learning algorithms are more suitable for binary classification?
We know that there are many different types of classification algorithms. But among the different categories of classification algorithms, which algorithms are suitable for binary classification and ...
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Suggestions for binary time-series-classification model for small dataset
Hopefully I´m at the right place for my question:
I´m looking for suggestions for models to use to classify multivariate time series. I´m trying to find a way of classifying the behaviour of motors ...
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How to generate classification rules using only positive values
Problem Description
I have a survey data set that I want to use for a classification problem.
In short respondents are grouped split along a binary target variable into "1" - part of the ...
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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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Binary Classifier , when Data Points are very less and number of features are very large [closed]
I am building a Binary Classifier.
There is no Real World Scenario Problem Statement, We have just given only the data set and some guidelines.
Number of features : 2040
All features are in decimal ...
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What could go wrong if I sample before classification?
I have a million entries in a table that I can use to train a binary classifier. Only 30 thousand of them are positive. Is there anything fundamentally wrong with selecting around 30 thousand negative ...
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Top 2% of scores of a binary classifier are 100% class 1
I have a binary classification model (Xgboost) that is supposed to be predicting whether a customer will be purchasing a service.
Overall the metrics are satisfactory ~.67 AUC, ~30% precision and ~40%...
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How to prepere dataset for binary classification (anomaly detection?) on timestamped sensor data (multiple files)?
my goal is to make prediction (good or bad data) on sensor data. I tried a lot, but failed to shape my data to get the desired output.
scenario:
I have multiple timestamped (time as it self is not ...
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Creating a custom layer in tensorflow
I'm trying to create a layer in TensorFlow, which works something like this:
And my implementation looks something, like this:
...
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