Questions tagged [feature-engineering]
the process of using domain knowledge of the data to create features that improve machine learning algorithms
349
questions
0
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1answer
20 views
Aggregating multiple encoded categorical values
I am trying find commonly used techniques when dealing with high cardinality multi-valued categorical variables.
I am currently using a dataset with a feature CATEGORY which has a cardinality of ~20,...
0
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1answer
19 views
How to do target encoding when data has repeated rows?
How can I do encoding for a category when data has repeated rows? Can I do target encoding? Or Is there another encoding I can use?
I want to figure how to include a categorical variable in a model ...
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0answers
11 views
is it beneficial to use high-order n-grams as feature vectors for web anomaly detection?
i am studying about the use of n-gram models to classify web attacks based on several parameters like, requested resources, query parameters and attributes, characters distribution and so on. Most ...
1
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0answers
22 views
Extract features from Decision tree leaf nodes
Recently came across a coursera course on "How to win Kaggle competitions" where they explain how we can engineer a categorical feature from each leaf node of the decision tree.
[Video Link][1]
I ...
1
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1answer
15 views
Measuring impact of missing feature on model performance
Is there any general approach to approximate how a missing feature will impact the prediction performance of a regression model?
For example, if I train a model using 10 features, but want to make ...
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0answers
19 views
Summarize events per ID
Data:
Each corresponds to an event (a person's visit to the hospital, as an example). I have a series of data associated with this event (duration of visit, motive, etc...).
Objective:
Summarize the ...
0
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1answer
26 views
Can we add other features along with Text in sentiment analysis
Can we add other features along with Text to a ML model . Like giving text and other features as one input combining them and predict the output value. As model can learn some more better if given ...
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0answers
19 views
Titanic Dataset - Feature Engineering - Ticket feature
I am currently building my first machine learning model using the titanic dataset.
After the data exploration, I decided to focus my attention on the 'Ticket' feature.
One thing I have noticed about ...
1
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0answers
19 views
Does EDA helps only in case of linear regression?
I know what Explanatory data analysis is and how it helps us investigate and understand the data. What I dont understand is how does this help in case of nonlinear relationships? I mean if I'm using ...
0
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1answer
20 views
What is the best way to encode an arbitrary collection of strings into int categorical variables?
I have a bunch of categorical labels which I want to transform into int categorical features for an ML algorithm.
The problem is I don't have a prior list of the categories, so that I can't just ...
1
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1answer
25 views
1
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1answer
41 views
Correlation based Feature Selection vs Feature Engineering
I'm a bit confused about the superiority of Feature Selection over Feature Engineering or vice versa.
Let's say I just want to get the best possible performance on a couple of models like a neural ...
0
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0answers
11 views
transformation and standardization. what will be the order?
I have a dataset with all positive numeric values for a classification problem.
out of 8 columns 4 columns have skewed distribution.
What will be the ideal order to follow ?
...
5
votes
3answers
488 views
What can be done with highly correlated variables (>.95 and <-.95)
I hope we can remove the highly correlated variables based on the feature importance may be with PCA etc.
Is there anything we can do with highly correlated variables/
Thanks in advance !
0
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1answer
40 views
How to handle potential interactions when one-hot encoding?
Let's say I have two categorical features: Movie, Director. I one-hot encode both the Movie and Director features for use in a linear regression model.
The problem is that two or more movies may be ...
1
vote
1answer
20 views
How do I encode time in high dimensional space?
I have a dataset of form text, text, category, category, time, text and I would like to apply the attention mechanism to it. This requires that all inputs be in the ...
0
votes
1answer
25 views
Modeling events at irregular intervals
I've got data representing a sequence of events at irregular intervals and an outcome. There are a fixed number of event types, about 10 of them. The outcome being modeled is binary.
Arguably the ...
1
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0answers
22 views
How can we assess the importance of the features even if we ended up applying PCA?
There are multiple techniques to analyze the feature importance (permutations, SHAP values, etc).
It is essential that, in order to improve the interpretability of the model, we can somehow map the ...
0
votes
1answer
157 views
Word2Vec and Tf-idf how to combine them
I'm currently working in text mining ptoject I'd like to know once I'm on vectorisation. With method is better.
Is it Word2Vec or ...
0
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0answers
18 views
Encode features for Machine Learning Model
I am working on a classification problem on medical reports. I am taking ngrams as features. The problem is that there are few attributes that a single ngram can posses. For example, if 'abdominal ...
2
votes
2answers
36 views
How much can the AUC improve comparing the raw dataset and the feature engineered dataset?
Let's say I put the following two datasets in the best possible model (same model for both):
A raw dataset, the variables as they came just from the query.
A feature-engineered dataset, with hundreds ...
2
votes
0answers
22 views
Difference between Gibbs sampling and variational Bayes inference
After reading in blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. Below link described but it's difficult to ...
2
votes
2answers
54 views
differences between feature weighting and feature selection
what are the differences between feature weighting and feature selection? And is feature importance like feature weighting?
1
vote
1answer
21 views
How to incorporate keyboard positions on character level embeddings?
I am working with NLP and have character level embeddings.
I have embeddings learned from Wikipedia text.
Now, I want to learn embeddings from chat data (where misspellings and abbreviations are way ...
2
votes
1answer
15 views
Does it make sense to expand word embeddings so that each array index is a feature input or should the embedding itself be a model input?
If you are building a DNN, say, with two layers, and you want to use embeddings as one of your feature inputs, what's the best way to input the embedding?
I'm trying to understand if I should break ...
2
votes
2answers
96 views
How to do feature selection after using pre-trained word embeddings?
I am working on a multiclass text classification problem. I want to use the top k features based on mutual information (mutual_info_classif) for training my model.
...
1
vote
3answers
75 views
why to use Scaler.fit only on x_train and not on x_test for normalizing value using MinMaxScaler?
while normalising the data everone is saying that we need to fit only on x_train and not on x_test ? why is that we should not fit x_test ?
if we should not fit the scaler on x_test then why we need ...
1
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2answers
42 views
3
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2answers
53 views
Prediction vs causation in a ML project
I am performing a classification task and was able to identify significant predictors (important features using Random Forest) that can help separate the classes or influence the outcome.
But I read ...
2
votes
1answer
120 views
How to interpret Shapley value plot for a model?
I was trying to use Shapley value approach for understanding the model predictions. I am trying this on a Xgboost model. My plot ...
1
vote
1answer
45 views
How to choose input variables for ML
Let's say I have a huge database with 100K records and 60 columns. Let's say one of the column is "min_p". What I do is apply some logic/rule to determine the output label for this record. Basically I ...
1
vote
2answers
75 views
why does making the target variable normally distributed helps?
while working on some regression problems I have found that if the target variable is skewed, making it normally distributed(using transformations) almost always helps. Why is that?
Should we also ...
0
votes
1answer
66 views
How to transform specific type feature to yield better prediction?
I have a dataset with 5K records focused on binary classification problem. I have about 60 features.
Out of 60 features, around 45-46 features are of 'Min' and 'Max' type.
For example, minimum blood ...
1
vote
1answer
54 views
How to distinguish informative and non-informative feature - Feature importance?
I have a dataset with 5K records focused on binary classification problem. I have more than 60 features in my dataset. When I used Xgboost, I got the below ...
0
votes
2answers
30 views
How to extract crucial features to create an image
Imagine, you have a dataset containing pictures of (example only, just to explain the task) cats and dogs. The data set is labeled, so we can train using supervised learning algorithms.
My goal is to ...
1
vote
1answer
52 views
Mismatch between optimum features and grid scores using RFECV?
I have a dataset with 5K columns focused on binary classification. I have more than 60 columns. I am trying to find the best features through RFECV approach. Though it produces 30 optimum features, ...
0
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0answers
17 views
how to log transformation works, In what case we need to do log transformation
I am a newbie to machine learning, I am doing binary classification problem, I have some data, IN that data one of the columns having some float data. I plot data, The Data looks like this
My column ...
0
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1answer
13 views
2
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1answer
12 views
Thoughts on Feature Engineering of a duration_in_program Variable
So I am trying to predict which customers would leave a loyalty program sponsored by X firm, using an ML classification model.
I further believe that the duration for which a customer has been in the ...
2
votes
1answer
25 views
is it a good idea to take the derivative or integral of some features and add them as new features in machine learning?
I'm learning how to do feature Engineering and come across some ideas in my head that's why I want to ask if I had some dataset with some features let's say 2 features and I have a timestamp column ...
1
vote
2answers
54 views
Unsupervised encoding of categorical features
I have multiple log records with discrete categorical features. Shape of my dataset is (100k, 24) My aim is to look for anomalies in these records. I am planning to cluster the data after encoding.
...
0
votes
1answer
32 views
Evaluating likelihood of egg breaking when falling in random container on concrete [closed]
I am working on a project where I would like to predict whether an egg will break if it is put in a container that is then dropped on concrete.
I am looking at the different factors that play a role ...
0
votes
0answers
27 views
Transform skewed ratio data (value range from 0 to 1) to reduce the skew
I want data clusters. Because my cluster algorithm doesn't work with skewed data I want to change that in advance. I have ratio data, i mean probabilities (values between 0 and 1). But these data are ...
1
vote
1answer
66 views
Can a recommendation system be used as a binary classifier?
I have a computer-generated music project, and I'd like to classify short passages of music as "good" or "bad" via machine learning.
I won't have a large training set. I'll start by generating 500 ...
4
votes
1answer
43 views
Overfitting due to features correlating with training set generation rules
As background, I am using a Deep Neural Network built using Keras to classify inputs into 5 categories.
The current structure of the network is:
Input layer (~450 nodes)
Dense layer (750 nodes)
...
2
votes
3answers
66 views
How can we convert time series data to supervised learning problem?
I am preparing a data for machine learning model. I want to deal with time series data as normal supervised learning prediction. Let's say I have a data for car speed and I have several cars models ...
0
votes
2answers
72 views
How to reduce the Root mean square error
I have dataset which describe "how many passenger arriving in some airport " and I would like to predict how many passenger arriving in monthly bases for next year. The features that I have is the ...
1
vote
3answers
87 views
Correlation between categorical variables based on the target distribution
Let $X$ be a category with very high cardinality and $Y$ be my target. when I look at $X$ distribution to $Y$ I see that some of the levels are very similar to each other . I would like to find a way ...
10
votes
1answer
201 views
Why does frequency encoding work?
Frequency encoding is a widely used technique in Kaggle competitions, and many times proves to be a very reasonable way of dealing with categorical features with high cardinality. I really don't ...
2
votes
3answers
54 views
Aggregate categorical feature by the target
Having a list of triplets {X1,X2,Y} such as :
{pennsylvania, fever , malaria}
{pennsylvania, headache , malaria}
{arizona, ketone smell , flu}
{new york, fever , cancer}
{ohio, hand pain , ...
