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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3 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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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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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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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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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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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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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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34 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
65 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
39 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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46 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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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
38 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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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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24 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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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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How to use feature group?

Let's say I have a data set like the following: ...
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1answer
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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
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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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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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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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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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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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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
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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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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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63 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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174 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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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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Creating Flags Instead of Designated Values

I'm working with http://archive.ics.uci.edu/ml/datasets/Bank+Marketing# dataset in order to create a model. We're going to use it in a presentation to introduce people our new data science environment....
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Should features be correlated on uncorrelated for classification and regression (prediction)

I have seen researchers using pearson's correlation coefficient to find out the relevant features -- to keep the features that have a high correlation value with the target. The physical implication ...
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Can feature representation acquired by a same model but trained on different corpus be used on the same classification model?

For example, if I wanna do document classification with doc2vec embeddings, first I train the training set to get doc2vec embeddings, and fit the embeddings to a classification model; later on when I ...
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LIME- how does sampling of data happen at observation( data point) of interest?

I was going through paper of LIME( Locally interpret-able model agnostic explanation) I am wondering how exactly data is sampled at point of interest to fit regression? https://arxiv.org/pdf/1602....
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Does the predict function in machine learning understand categorical data

I understand that before feature engineering one has to split the dataset into train and test data, so as to avoid bias in the analysis. I also understand that the machine learning model does not ...
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Feature Vectors representation

I would like to know I how you represent a feature vector like this dataset wise. The vector length is dynamic but the each element has a fixed length (9). For xgboost implementation, do I just create ...
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28 views

how to use sklearn without feature selection

I am trying to study the effect of using feature selection onmy text classification code . I want to make a rating without any feature selection, but sklearn use document frequency (df) by default ...
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Can I merge levels or factors having equal mean in categorical variable

I compared levels of categorical variable by their respected mean, obtain from continuous response variable using pivot table. I found that some of the levels is having nearly equal mean e.g 'BrDale' ...
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Extracting Features for Graph transformation

Suppose I have a directed graph G (V,E) whose transformation is defined by a library of patterns. Each vertex is of particular type. The library of patterns contain subgraphs (g1,g2,g3 etc)and it's ...
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Feature engineering ideas with dates, coordinates and other variables

I'm working on an ETA problem where I'm trying to estimate a time of arrival for a delivery. I have coordinates of pickup/destination, time of pick , infos about the rider, some other variables that ...
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1answer
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Feature engineering - house price prediction (small dataset) [closed]

I am working on the task of predicting real estate prices. My dataset has only 10 variables described below. I'm thinking about feature engineering but nothing comes to mind. Variables: ...
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1answer
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Why does removal of some features improve the performance of random forests on some occasions?

I completed feature importance of a random forest model. I removed the bottom 4 features out of 17 features. The model performance actually improved. Shouldn't the performance degrade after removal of ...
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Accelerometer and Gyroscope features

I am having accelerometer and gyroscope reading along x,y,z axis and want to get motion direction info at each time step. What all feature extraction would be best suited for this type of requirement. ...
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Optimal practices to group data by Customer ID for churn prediction

Here's a quite common problem and I read a couple of questions/answers on it, however I still having my doubts about what are the best practices for grouping data by Customer ID for churn prediction. ...
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25 views

How to interact two variables in python?

I have about 8 features as my predictors in a logistic model I am trying to fit in python. One of the features is TotalAward (Financial Aid) and the second is NEED. I am attempting to predict the ...
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Variable importance of Numerical features in Classification Model - Random Forest Classifier

I have few numeric features in my model. Out of 25 features, I have 7-8 numeric features in my model. One thing I observed is model gives more weightage to numerical feature compare to categorical ...
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32 views

How to Avoid rarely used discrete feature values in a dataset

On Google's ML crash course it states: Good feature values should appear more than 5 or so times in a data set. Doing so enables a model to learn how this feature value relates to the label. ...
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1answer
51 views

Aggregate Categorical Data

I have a scenario in which I'm required to run my analysis at the Account level. One of the features that I'd like to look at is the no. of subscriptions against an account. There can be multiple ...
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55 views

Dealing with NaN for predictive models

I have data set that has data for patients: Arrival_Date : is when the patient has arrived Seen_By_Nurse : is number of minutes patient take to be seen by nurse since arrival when value is NaN it ...