Questions tagged [feature-engineering]

the process of using domain knowledge of the data to create features that improve machine learning algorithms

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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,...
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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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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 ...
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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 ...
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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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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 ...
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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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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 ...
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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 ...
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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 ...
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1answer
25 views

feature importance and xgboost?

Let say I got feature importance for xgclassifier ...
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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 ...
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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 ? ...
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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 !
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...
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1answer
13 views

How to use feature group?

Let's say I have a data set like the following: ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ...
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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) ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 , ...

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