Questions tagged [binary-classification]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0
votes
0answers
15 views

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 ...
0
votes
0answers
17 views

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 ...
1
vote
1answer
14 views

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 ...
2
votes
2answers
551 views

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 ...
1
vote
0answers
9 views

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 ...
0
votes
0answers
12 views

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 ...
1
vote
1answer
22 views

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 $y_i$ ...
1
vote
1answer
27 views

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 ...
2
votes
3answers
63 views

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 ...
2
votes
1answer
38 views

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%...
1
vote
0answers
44 views

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 ...
0
votes
1answer
8 views

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: ...
1
vote
0answers
35 views

How to deal with multiple binary timeseries?

I have a time series data looks like this userID month year target user1 1 2 1 user2 12 2 0 ... ... ... ... userN 6 3 0 with about 2000 unique userID, ...
3
votes
1answer
30 views

How to combine binary classification with patient stratification?

I am working on a binary classification model (healthy/diseased) based on gene expression data of different patients. As a second task, I would like to stratify these patients and find subgroups. I ...
0
votes
0answers
31 views

TSNE interpreration and separability

I have a binary classification problem where I train a neural network on a training and validation data sets. But I am not satisfied with the performance of my trained classifier (the NN above). The <...
1
vote
2answers
159 views

Python xgboost predicting future events

This is related to this article: https://towardsdatascience.com/forecasting-of-periodic-events-with-ml-5081db493c46 I found it interesting and tried to replicate it, having as a result a xgboost ...
0
votes
2answers
39 views

If I have two variables with strong correlation, should I delete one and leave the other in my data

I have a large dataset, where I should make a binary prediction. The fact is that, after analyzing the data, I found that some variables are positively correlated to each other. So, I was wondering ...
0
votes
2answers
35 views

How to deal with Different Shapes of X_train and X_test after OneHotEncoding?

I am trying to perform OneHotEncoding as well as feature scaling on my training and testing data separately, steps I did: ...
0
votes
1answer
29 views

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 ...
2
votes
3answers
39 views

How are scores calculated for each class of binary classification

The formula for Precision is TP / TP + FP, but how to apply it individually for each class of a binary classification problem, For example here the precision, recall and f1 scores are calculated for ...
0
votes
0answers
11 views

Best Approach for Predicting NFL Betting Outcomes

I play a game every year with my family, where we compete to make picks against the vegas odds for each NFL game. We aren't actually betting any money, but instead we each try to make the most correct ...
1
vote
1answer
23 views

Binary document classification using keywords for a very small dataset

I have a set of 150 documents with their assigned binary class. I also have 1000 unlabeled documents. Each document is about the length of a journal paper. Each class has 15 associated keywords. I ...
0
votes
1answer
26 views

Excluding data via confidence score: Is it a good idea?

Let's say I have a model which has a binary classification task (Two classes of 0 and 1) and therefore, it outputs a number between 0 and 1, if it is greater than 0.5 we consider it to be class 1 and ...
3
votes
1answer
172 views

How do you add negative class sample for binary classification?

How do you prepare the negative dataset for binary classification? Let us say that I am building a classifier that has to classify whether the input image is of a car or not. I already have a dataset ...
0
votes
0answers
12 views

Non-Classification outputs in a classification problem

maybe this is a very stupid question, so please excuse me as I am a total beginner in Machine learning. I have a dataset divided into X (shape: 10000, 599), Y(shape: 10000,). Y is simply zero or one. ...
0
votes
0answers
30 views

Should I balance the test set for predicting probabilities in binary classification?

In my dataset I have 75 % of "0" class instances and 25 % of "1" class. It a real world ratio between classess. I balanced my training set, and trained the model. Then, depending ...
0
votes
1answer
16 views

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 ...
2
votes
1answer
27 views

Is it ok for precision and recall metrics if a small minority of samples are both false positives and true positives?

I am working on a multi-label classification NN using genomic data. there are 10 samples and 2 ground truth labels (age and gender) for every sample. I use a sigmoid activation at the final layer and ...
1
vote
1answer
45 views

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: ...
1
vote
3answers
124 views

logistic regression or density estimation for binary dependent variable and binary (or categorical) features [closed]

I have a binary dependent variable $t$ and categorical features. We can even simplify to binary features since I can one-hot encode the categorical variables. In practice the one-hot encoding induces ...
0
votes
2answers
78 views

Binary Classification with Imbalanced Target [closed]

I have a dataset and my objective is to run a Binary Classification, but my target feature, that is supposed to have "True" and "False", only has "True", as a value. I ...
1
vote
2answers
100 views

Why does my InceptionV3 model give a high training accuracy (99%), a high validation accuracy (95%+) but a very low testing accuracy (55%)?

Note: Please go through this in its entirety. My objective here is not just to get a high testing accuracy but to explain why it is so low in spite of validation accuracy being so high. I am a ...
0
votes
0answers
13 views

How does L1 normalisation work in Binary Classification?

I was working on a project where I was using TF*IDF algorithm. After applying grid search, I got the tfidf_norm=l1. Can someone explain how L1 normalisation form works in binary classification?(I have ...
0
votes
0answers
73 views

using Reinforcement learning for binary classification

I want to build an agent for binary classification. I have a large dataset with two label (0 and 1). I want to build an agent to predict labels. I build a deep model and now I want to build an agent. ...
1
vote
0answers
58 views

How to pass manually split data to cross-validation

I have to perform a binary classification. My dataset is quite small 280 samples and quite imbalanced (1:10 ratio). I kept around 100 sample as testing and about 140 for training. My input variables ...
2
votes
1answer
297 views

How to choose the right threshold for binary classification?

I am currently working on the titanic dataset from Kaggle. The data set is imbalanced with almost 61.5 % negative and 38.5 positive class. I divided my training dataset into 85% train and 15% ...
1
vote
0answers
27 views

Testing a Binary Classifier

I have been training a binary multilayer perceptron on a database made out of roughly 3600 0 values, and 4 1 values. Afterwards, I'm testing the MLP on a test set made out of 7 0 values and 7 1 ...
0
votes
2answers
41 views

How can compare suggestion models with different performances?

I have 4 class binary classification models. That models identify which class a particular students is suitable for. For example, we have user 1 and 4 classes ...
1
vote
1answer
24 views

ZeroR as performance baseline for binary classfication model?

It is known that ZeroR model is used predict the majority class in a given data set. Having said that, is ZeroR a suitable performance baseline provided one has a balanced data set (50/50)? If not, ...
0
votes
1answer
62 views

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/...
0
votes
1answer
37 views

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) ...
1
vote
0answers
46 views

Classification problem with 2 level features

Consider an automated house, where we can collect router data every minute. The objective is to predict router fail/no fail (1/0) in a future time window. The router sends data at two different levels ...
0
votes
0answers
36 views

Performance metrics for balanced binary classification

From my understanding the reason we use Confusion matrix, Precision-recall, F1 score is because when our datasets consists of imbalanced class labels accuracy can be misleading. However what if ...