Questions tagged [feature-engineering]
the process of using domain knowledge of the data to create features that improve machine learning algorithms
479
questions
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1answer
25 views
Explanation of random forest performance difference to when using categories and when using dummy variables
I have some hand coded feature which is a category with values "High", "Low", and "Normal".
I created this feature myself and my problem performance (classification) ...
0
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0answers
18 views
Extracting features of the data using statistics? [closed]
May I know which statistical approach is best suited in data science to introduce new features or extract hidden features of a given dataset?
For example I have a following dataset of rectangular ...
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0answers
11 views
Classification based on color clustering
I need to classify some domain specific images by analysing their color distribution.
I have annotated data; this last classification step is supervised.
After some preprocessing and masking and other ...
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0answers
10 views
Do Any Frameworks Provide Better Support for End-To-End Integer-Based Feature Engineering, Modeling, and Inference?
A retail enterprise I work with with wants to switch from its home-grown time series data analysis and prediction system to something more established and with community support. One unique feature ...
0
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1answer
12 views
when will the incareful features harm the model?
I am working on financial prediction problem(time-series prediction problem).
I think feature engineering is importance in this problem. So i am careful to check the feature's effectiveness. And i ...
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2answers
70 views
How to read the labeled enron dataset categories?
I am trying to use the labeled Enron dataset (link) but I am really confused about the labeling system they use. I understand the Cat_[1-12]_level_weight is some ...
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0answers
15 views
Which are the features selection techniques depending on the combination on cat num columns in independent and dependent features?
I am very confused:
For what I understood I should:
Multicollinearity check with Pearson corr and possibly consider to drop multicolliner features
Then?
I am very confused feature selection should be ...
0
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1answer
12 views
How to aggregate features to a group level as a feature in machine learning model?
I am building a model to predict some behavior at a household level. I could roll up income or number of cars etc so that I can take everyone into consideration. But how can I roll up something like ...
1
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1answer
28 views
What is the intuition of using clustering for performing feature engineering in machine learning tasks?
I am trying to implement the research paper Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance. The paper has a section called meta feature engineering where they have ...
2
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1answer
32 views
How do I combine predictions from classifiers for two different problem?
I am working on a classification problem for predicting whether the shipment is going to be late or not.
I would say the classifier is mediocre at predicting the positive class at the moment. But the ...
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0answers
27 views
Is my idea of a Feature Store wrong?
Cross-posted on Reddit ML.
Should a Feature Store be part of an enterprise data catalog?
To me, a feature store seems to be a highly niche data catalog but missing a lot of the benefits of having an ...
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0answers
34 views
How do I aggregate cross-validation results for per-sample insights?
I'm trying to do feature engineering for point cloud data with three classes, and after having finished implementing the most obvious and simplest features with somewhat good results I'd like to use ...
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0answers
19 views
Is there a way to implement nested features in unsupervised models?
Our project has built an unsupervised model that uses data about a number of companies. Some of these companies are public and some are private. The ones that are public have much higher financial ...
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0answers
26 views
How can I transform a sequence into features
When Machine Learning libraries don't support categorical features those features can be one-hot encoded into a series of binary feature columns. I have a feature that represents a sequence or ...
0
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1answer
27 views
Cyclic dependency between feature and predictor class
I have a feature which has specific categorical values ex(Technology, Hardware, Software, Marketing, Evnts etc). Based on this and some other features, I am trying to classify the dataset into 2 ...
1
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0answers
18 views
Serving feature pipeline [closed]
Imagine, there is a service, providing credit history for customers in form of list of his loans. Let's call it my-loan-service. For the sake of simplicity - I can ...
0
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1answer
13 views
Are there readily available models that can handle conditional correlation?
I've been working my way through the features of the Kaggle House Prices dataset (Note: this is a non-ranking entry, so this is just for exercises), and I've found a couple situations where there is a ...
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0answers
13 views
Not able to get a good accuracy score for the classification problem
I have taken a music popularity dataset which has five classes based on the popularity of the songs.I have made a Random forest model to predict the popularity of a given song(given its features).I ...
0
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1answer
18 views
Problem with binning
I am trying to change continuous data points to categorical by using binning. I know two techniques, i) equal width bins ii) bins with equal number of elements.
My questions are:
Which type of ...
0
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1answer
10 views
What is the best way to feature engineer features which have more than one repeated values?
What is the best way to feature engineer features which have more than one repeated values ?
I want to parse this data and finally keep in a pandas df for further analysis.
Example,
I have data of ...
2
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1answer
41 views
Feature Interactions vs Feature Importances [closed]
Can somebody explain in-detailed differences between Feature Interactions and Feature Importances? And their applications?
0
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0answers
8 views
Is adding geo information to zip codes redundant in feature Engineering?
I was wondering if it is redundant to add geo information like elevation and distance between two points (between supplier and purchaser) as features to a model, if you already have country code and ...
0
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2answers
32 views
How to train a model to predict if 2 samples refer to the same thing?
I have 2 ddbb with around 60.000 samples each. Both have the same features (same column names) that represent particular things with text or categories (turned into numbers). Each sample in a ddbb is ...
1
vote
1answer
26 views
Feature importance of random forests
I have a dataset with 11 features, I noticed that manipulating these features (eg dropping one or some of them) doesn't affect the error scores of training and testing data, so I had to check the ...
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0answers
20 views
Is my feature normalization correct?
I have got a X = mx3 array of input data and y = mx1 array of desired outcome data. Now I want to normalize the features ...
1
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1answer
30 views
Adding extra (meaningful) features does not improve model performance
I am struggling with confusion matrices and their outputs. I thought to follow all the steps right, but unfortunately it seems that something is not going well.
I had a dataset built and labelled on ...
0
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0answers
12 views
How to interpret feature weight coefficients in logistic regression for text classification?
I am working on a simple text classification problem where I have as inputs tweets and as class whether that tweet contains fake news or not (0 is real news, 1 is fake news). I have trained a logistic ...
0
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0answers
47 views
Convert time series data to supervised learning problem
I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each.
I am trying to frame this time series problem as a ...
1
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0answers
16 views
XGBoost with deep trees
I've been exploring the use of XGBoost in many different applications. Up to now, I always find the best results with shallow trees (from 1 to 3 levels), with the rest of the parameters very dependent ...
0
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1answer
24 views
Proper feature selection methods for classifying signal into two category
I have a confusion to decide which feature selection method that I should employ in my research whose objective is to analyze which features that are significant in representing a certain condition of ...
0
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1answer
38 views
What features used by CNN model should a feature store actually store? [closed]
According to MLOPs principle, it is recommended to have a feature store. The question is in the context of doing image classification using deep learning models like convolutional neural networks ...
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0answers
13 views
Transforming binary data for decision trees
I have binary columns in my dataset (20) e.g. hot_weather, discount (y or no), where in each case 1 = yes no = 0. I am using this data on tree based methods.
It is a regression problem and my RMSE is ...
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0answers
10 views
Reversing data through a tensorflow feature_column.embedding_column
I am building an variational autoencoder in Tensorflow, and one of my columns has object data. The data is too sparse (on the order of 2^16 possible values) to use one-hot encoding, it's not ordinal ...
1
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2answers
33 views
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 ...
0
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1answer
28 views
Can I deal with a missing not at random column by creating a new column? (Feature engineering)
Task: Binary classification
Example problem:
Let's say we have two feature columns A and B. A has no nulls and is a binary column if a user completed an action (=1), 0 if they didn't. For all users ...
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0answers
48 views
Features in classification problem
It's rather a strange question about feature engineering for classification problems (churn). I've read a lot of articles and tutorials for such problems, especially on telco domain. In my case the is ...
1
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0answers
14 views
Encoding Data for ML Modeling with Key Value Pair
Apologies in advance for the poor title. I didn't know exactly how to phrase what I want in a succinct manner so hopefully I can elaborate a bit more.
I have a dataset where I have customer_id, ...
1
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1answer
23 views
Clustering using both text and numerical features
I have a dataset that contains 2 types of features, one is generated from doc2vec and one is numerical feature. I would like to perform clustering analysis on them. However, due to the size of doc2vec ...
4
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2answers
39 views
Machine Learning with intended missing values
I have a dataset relating to humans completing reviews, the target variable is whether the review decision is correct / incorrect and one of my features is a trailing 4 week accuracy score for the ...
2
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1answer
38 views
Cannot understand feature extraction
I'm following an AI course and we've just entered the deep learning chapter. Speaking about the difference between classic machine learning models and deep learning, it turns out one of the most ...
1
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0answers
19 views
Feature engineering and longitudinal data
I need some advice for my feature engineering.
Suppose I have 90 days follow-up data. on 12 patients and I have the vital status of the patients at the end of these 90 days (deceased=1, alive=0)
...
1
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1answer
24 views
What is a good approach for embedding both textual and spatial features for document classification?
I am working on a document classifier that can perform the classification based on the document structure as well. My plan is to get the word embedding as well as the word coordinates and somehow ...
0
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0answers
4 views
What are good feature selection and engineering approaches for data with known uncertainties?
Context: I am working with a set of geological features that could have uncertainty values attached to them (for example, values come from drill holes that are sparsely distributed and must be ...
0
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0answers
16 views
Feature Engineering and prediction with R and python
I have a sequential dataset, and have 2000 rows for 300 ID. I have 20 variables (i.e in my real dataset)
...
2
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3answers
43 views
Is it possible to change the input columns of a trained ML model while making predictions from it without affecting the accuracy?
Consider the following scenario.
I have trained a K-Means model on some input features, say, (A, B, C, D and E).
Now at the time of making predictions I want to make the model predict using only fewer ...
1
vote
1answer
43 views
How to model a supervised recommender system with varying data
Suppose there are 2000 movies and a company wants to recommend some movies (for example, at most 5 movies) to each visitor. The objective is to learn how to predict which movie will be selected if a ...
2
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1answer
50 views
Should you use the same algorithm in your feature selection as your model
My question is should you use the same algorithm in feature selection as your model?
If I'm using a KNN model for classification should I also use a KNN algo when running feature selection? Or ...
0
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0answers
18 views
how do i preprocess percentage data?
I am analyzing a problem where i have 5 diseases and the measure of effectiveness of a remedy in its 7 first applications. The data is organized as an Excel spreadsheet as follows:
(The spreadsheet ...
3
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1answer
35 views
How best to use the resale transaction year in predicting housing prices?
I'm looking into the classic problem of predicting apartment prices (resale market) based on the their type, size, location, etc. Pretty straightforward and Linear Regression or Regression Trees give ...
0
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0answers
15 views
Converting the continuous numerical features into gaussian distribution and how to deal with NaN values after that?
I have a dataset in which there are few continuous numerical features that distribution over them is non gaussian and this means, skewness is nonzero (positive or negative).
I read that it is good to ...
