Questions tagged [features]
The features tag has no usage guidance.
99 questions
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I am trying to build a logistic regression model
I have a time series data of which a family have spent money on different products. Each product is allocated to a category ( it can be a two level category path ) for eg- (Food > Chicken) or (...
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Are there any general theoretical results about the behavior of data in the neighborhood of a single data point?
I know from calculus that any relatively well-behaved function $y=f(x)$ can be approximated by a linear function $y=ax+b$ within a sufficiently small neighborhood around each point of an independent ...
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Logistic regression on unknown features
Consider the following problem: I want to classify data into classes. The features have names, and for example, assume that the names are lowercase words in english. We assume that not all elements of ...
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How to build a model where each data point has different levels of information?
Let’s say I want to predict the weight of a person given information about them; height & sex.
Now, let’s say that that I have additional information about roughly 50% of the individuals included ...
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33
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handling predictions with optional or missing features
We have a few variables that are highly predictive in our modeling task. Is it sound to train models with a superset of features even though some are known NOT to be available at predict time? & ...
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34
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Robustness and Sensitivity of Naive Bayes to Irrelevant Features
I understand that one of the strengths of Naive Bayes is its robustness to irrelevant features. However, it's also important to note that it can be sensitive to the presence of irrelevant features, ...
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152
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will ANN identify a feature that has no influence?
I am doing my first steps in training ANN, one of the features in my data X is user_id.
Assuming ...
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31
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How can I quantity feature importance while performing unsupervised clustering with mixed data types?
I wanted to cluster data points into 2 clusters, I am using clustmix package from R
I wanted to understand importance of each of the feature, I have 203 features. I have tried featureimp package from ...
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59
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Encode 10k features where each feature is having more than 500 categories
I have around 10k features in my dataset and each feature is having more than 500 categories. what is the best encoding method to convert this categorical features to vector form?
"span_dir":...
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241
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Calculating correlation between embedding features
I am looking for a technique to calculate correlation for embedding features (array of floats). I'm interested in the correlation between features (embedding-embedding) as well as between feature and ...
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21
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Formal terminology: metafeatures then groupby (GROUP BY) operation
In ML training and other analytics I often combine features to produce a 'metafeature' and then perform a 'groupby' (pandas) or 'GROUP BY' (SQL) query. What is the technical term for this operation?
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How many features is too many when using feature selection methods?
Now obviously there is no such thing as an ideal number as every problem is different, but I've been Googling, ChatGPTing, & Youtubing this question for a few days now and I am constantly getting ...
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Time series and regression with dataframes as features
I am working on a data science project where I have 4 different dataframes representing 4 different metrics (let's say, met1, met2, met3 and met4). These metrics are time series and each one of them ...
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76
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Feature Importance in Stacked Model
I have built a stacked model using mlxtend StakingCVClassifier. I want to know the feature importance scores now. Is there any way I can calculate feature importance scores for the stacked model? If ...
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545
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How would I design a database structure for a feature store?
I have a personal project to create predictions for tennis matches.
It currently consists of a Python application and a MySQL database. I extract data from various websites and APIs and store it in ...
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103
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Best Feature Extraction Practise for Long Audio Data
I have a video dataset and my aim is classifying predefined scenes in these videos at 1fps (that means I perform classification at each second). Therefore, I plan to fuse audio and visual features for ...
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How to handle similarity search on mixed data types vectors?
I think this question is one that many beginners run into and I could not find a decent generic guide for it.
My issue is the following. I want to evaluate similarity of vectors which have mixed data ...
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31
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How to fit n features in a number of neurons smaller than n
Suppose I have a feature composed by 784 numbers, and I want to use it as input of a neural network implemented from scratch whose first layer has 64 neurons.
How can I put 784 numbers in 64 neurons?
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Should highly correlated features be removed, even if they have different type of information?
A quick example for this: we have many feature and two of them are policy count and premium_total (for all policies). We are predicting the expected claim amount with GBM or RF. Both policy_count and ...
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Shapley Values - How to interpret each value for each feature for a specific instance?
I am using Shap Values(the 'shap' module in python) to help me understand a bit better the relation between my features and my target. I am currently working on a binary classification problem.
I know ...
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How do I match the number of the features of new text data to the data used in the training of the model
I am working on a classifier for some twitter data to predict who it was tweeted by. I am only using the text of the tweets to build the model. After all text related preprocessing here is how I ...
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How to select relevant columns from a dataset with many features
I have a dataset with a large number of potential features (>100) and I am interested in finding a relatively small subset of these (maybe on the order of 5, or 20) features which is best suited to ...
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How to use a material number as a feature for Machine Learning?
I have a problem. I want to use a classification algorithm. I also have materialNumber as a column. Could I use that as a feature for my Machine Learning algorithm? ...
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71
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Create features for each row or only for a specific value
I have a problem. I want to predict when the customer will place another order in how many days if an order comes in.
I have already created my target variable ...
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Reverse engineer PII sensitive data from Inceptionv3 pre-trained model generated features
I'm using the pre-trained Inceptionv3 to build out features from proprietary documents. Some of these documents contain sensitive PII data. I use the 2K output from the second last layer as the ...
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138
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Hard time finding literature on feature clustering using Principal Component Analysis
Im new to StackExchange, so i am sorry if this is not the right way to ask a question on StackExhange.
For my thesis I wish to propose a methode for future research on using PCA to cluster features (...
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23
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Relation between Features & Polynomial Equations in Machine Learning
In Machine Learning, if the data we are working on has, say, 6 features/variables, does that mean the prediction line/curve of our ML model is represented by a Hexic polynomial equation whose degree ...
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762
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Feature engineering before splitting
This is a sister post to the original closed post (here). Since the data transformation part is done after data spliting on the TRAINING data only, I wonder wouldn't such transformation has dependency ...
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Is there a multi-modal population based metaheuristic that is non-GA?
I have a feature set from which I want to select various combinations and permutations of the features. The length of a solution feature vector can range between , say 5 - 20 features , and the ...
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1k
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Is there a way to combine multiple ML models where each use datasets with different features?
I have a dataset where some features (c,d) apply to only when a feature (a) is a specific value.
For example
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318
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train-test split on forecasting a time series using external features
I have a question regarding the train-test split when forecasting a timeseries using features instead of the time series itself. I know that I should use a time-based train-test-split if i use lagged ...
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Finding attributes that make up dense clusters of fraudulent transactions
I have data about purchases customers made in my website.
Some users later decline the purchase, a scenario I'd like to avoid.
I have lots of data about the purchases made in my website, so I'd like ...
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21
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vertical or horizontal storage of timesteps in feature store
I'd like to use a feature store to store some time series and I asked myself what's the best way to store the timesteps. Is it better to store each timestep horizontal and then doing windowing after ...
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Query regarding the 'Data type' of features in Machine Learning
Should all the features in a dataset be converted to the same data type? For instance, if all the features have numerical values, some int & some float, should they all be converted to float? What ...
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470
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Training & Test feature shape is different from number of columns in dataset
I am making a Sequential Neural Network for regression with 3 dense layers which will be trained on a simple dataset. But before I even get to that part of the code to execute the model I am getting a ...
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Can regression algorithms treat multiple input features as a single feature for prediction?
I have a regression problem where I have multiple RSSI values from 3 beacons and I need to predict the x & y coordinates of the mobile object sending the signals based on the RSSI values captured ...
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How to choose feedforward architecture for few number of features but very large instance?
Assume I have 1 million of data instance and each instance contains 100 feature. For each instance, I also have a lable.
The ...
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How to build multiple variable regression having a mix of numerical & categorical features?
There is a need to estimate Annual Average Daily Traffic Volume (AADT).
We have bunch of data about vehicles' speeds during several years. It is noticed that AADT depends on the average number of such ...
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243
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Non-commutative distance formula
I am trying to find a distance formula or a method that can give the non-commutative distance between two points in a feature space.
Suppose there are two movies represented in an R^n feature space. ...
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Integer encoding and weighing when one feature consists of more names [closed]
Hello I am trying to make a content based movie recommendation system and one feature is genre of the movie. I will give an integer number to each genre randomly. However, some movies are of more than ...
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614
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Resampling : My dataset is categorical or numerical?
I have a dataset with 203 variables. Like age>40 (0 -yes, 1-no), gender(0 or 1), used or not 200 types of drugs (one hot encoded into 200 variables), and one target variable (0 or 1). This is an ...
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Using partially defined features in an unified deep learning model
Suppose we have two types of feature A and B. A is defined for all kinds of samples while B is only defined for some of the samples. Here, B is partially defined does not mean B is missing value (such ...
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Structuring extensive medical histories + demographic information for prediciting future medical outcomes
I'm looking for advice structuring extensive medical histories for predicting future outcomes, specifically hospital admissions.
Let's say I want to predict the whether or not someone will be admitted ...
- 111
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Latent space for cross domain numerical features
I would like to find the shared latent space between two set of features. I have source and target domain features already extracted from images. I have 4 set of feature vectors for normal and ...
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820
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How SHAP value explains contribution of features for outliers event?
I'm trying to understand and experiment with how the SHAP value can explain behaviour for each outlier events (rows) and how it can be related to shap.force_plot(). ...
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Does binning a time series with pd.qcut (using quantiles) create data leakage?
Let's say I want to predict whether a company will default on it's debt at some point in time (so binary classification) and one of the time series variables I'm using is the "revenue" of ...
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1k
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Multi-Feature One-Hot-Encoder with varying amount of feature instances
Let's assume we have data instances like this:
...
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501
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112
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combine two features into one [closed]
In an epidemic disease dataset of 3 months, I have a feature (var dt_died) with the death dates of patients (800 people died out of all 12k unique subjects in this dataset, so obviously only dead ...
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622
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Feature importance difference in two similar machine learning models
Situation 1:
I have trained a text classification model (Model 1) which gives me a probability of true class as X. I have also trained a classification model (Model 2) using only the categorical and ...
