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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16 views

Handling different length string features and prediction of these based on other features

I am currently working on a problem where the dataset contains 200+ features (Let's call them the code features, e.g no.of.loops, memoryInst, loadInst, etc and Flags that are used to compile code ...
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How to perform a 1-way ANOVA right after One-Hot-Encoding

I am at the phase of dimensionality reduction. I am trying to figure out which categorical columns I should keep for my model and which I should discard. Because some of my categorical columns have ...
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One feature - several units

I have a dataframe where one of the features is the Mileage expressed in some cases in $\frac{km}{l}$, while in others is expressed in $\frac{km}{kg}$, according to the combustion type of the car (so ...
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How to automate ANOVA in Python

I am at the dimensionality reduction phase of my model. I have a list of categorical columns and I want to find the correlation between each column and my continuous ...
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Problem of finding best combination of features when desired feature is feature some_feature_A/some_feature_B

Problem is stated: we have giant csv file with one target column and rest are inputs, we don't know these features impact target but we would like to use algorithm that besides using linear and non-...
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How to transform stock data for LSTM-based neural network

I am trying to classify stock returns using an LSTM-based neural network. I would like to use closing price and volume as features (see below), but am unsure of whether I need to transform these (e.g....
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How to handle associated features in machine learning

I am working on a classification project in which some features are linked and I'm not sure how to handle them. I will simplify my project like that : There are different jobs, and multiple ...
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How to handle “ordered” features?

I have a dataset with weekly sales figures, and trying to building a classification model (predict stock-out). I want to use some feature(s) to tell the machine that: Week 1 comes after week 0 ...
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27 views

Null values as useful information for feature engineering

I'm preparing features for a neural network which I'll run in Keras and TensorFlow. The features are generated in Oracle. There I also replace null values. I'm not doing normalization on the database ...
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36 views

How to find the mean of a column relative to another column?

I am working on the Boston house price prediction. I have a column named GarageYrBlt that holds the year the garage was built for a specific house. My assumption ...
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How to handle missing date data?

I have a column named GarageYrBlt which just lists the year the garage of that house was built. I have one nan value for this ...
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How to use the fillna method in a for loop

I am working on a housing dataset. In a list of columns (Garage, Fireplace, etc), I have values called NA which just means that the particular house in question ...
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How to interpretate multiple histograms corresponding to each feature in multiple linear regression for relationship?

Used matplotlib to plot the histograms for each feature in Boston dataset available in scikitlearn library. How to interpretate the histograms to determine the correlation or significance of that ...
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Tensorflow “canned” estimators unable to “see” that signficant signal is the ratio of two columns

I have an appropriate dataset composed of X,Y and a LABEL to be used for binary classification. When fed to a Tensorflow canned estimator (tf.estimator.LinearClassifier, I tried both Linear and DNN) ...
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Feature engineering for categorical variables

I have some categorical variables in my dataset for a regression problem. 1) One of the variable can take 3 values (Girls, Boys, Girls&Boys). Converting it into one-hot encoding or binary ...
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Handling collection of featurevectors for classification

I have a data set where devices are represented by a collection of variables. These variables consist of several properties like a name, datatype, driver, limit values, etc. (mixed data; quantitative ...
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Feature engineering keywords for keras network

I am building a multi-label classifier, using the keras sequential API. My goal is to take an input of keywords 'cat', 'dog', 'rabbit' and apply a custom label. My ...
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34 views

How to treat a categorical variable which can have multiple values in regression?

I am working on a regression problem and has a categorical feature 'CATEGORY' which can take as many as 1600 categories. On top of that, each observation can have any number of categories in that ...
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How valuable is a categorical feature that has a predominant category over all other ones?

Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones? In data prepossessing step for "...
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Transformation of non-categorical discrete feature

Goal: Predict a performance score of a place of interest in a given city based on (amongst others), the number of restaurants within 200m. $\\$ Dataset: $D$ with a feature $x$ indicating the $\...
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How to scale exponential data for a regression problem?

I understand that I should be scaling features between (0, 1) before feeding them into a neural network. However, what happens if future data could be larger than my current training data? For ...
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How to automate the encoding process?

I am working on the Boston challenge hosted on Kaggle and I'm still refining my features. Looking at the dataset, I realize that some columns need to be encoded in binary, some encoded in decimals (...
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Potential problems with expanding training set

The problem is a binary classification one. My dataset contains users with activity over multiple days, where they all start with class 0 and can become class 1 after a certain activity (which is not ...
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If categorical variable has some hierarchy, should I just label them or split into dummy variables (One-Hot encode)?

I have a column which has 5 unique categories. There's a hierarchy between these categories (Best > good > OK/Not Sure > Bad > Worst) In this case, should I label them based on hierarchy like: ...
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Define difference between feature selection and feature reduction [duplicate]

What is the difference between feature selection and feature reduction? When do we use feature selection and what happens when we don't use it? How is this different than feature reduction?
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Actually using scipy.signal.stft multidimensional array as a feature

I'm trying to improve my score in the LANL Earthquake Prediction challenge on Kaggle extracting more features from the acoustic data through STFT transforming. Eventually I've found this well written ...
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Can I add features that are parts of another feature?

I am building a model (implementing both logistic regression and Xgboost) to understand the importance/significance of each feature in whether a customer is going to repurchase to understand what ...
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feature extraction for Radio signals classification

I found some code where the developer is trying to solve the problem of "Radio signals modulation classification" in this link link one of the solutions is using SVM to solve the classification ...
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Feature Importance Scores Python

I have a dataset having 7 attributes viz., time, C1, ... C7 pertaining to earth quake reports where each column/attribute represents a certain aspect of damage viz., power, sewer_and_water, ...
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Hand-crafted decision tree inspired from learned decision tree

Goal of this question: As I am the only 'machine learning guy' in our group, I wanted to get an outsiders view, that is a sanity check if what I am doing adheres at least to 'decent practices' in ...
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How to do feature engineering to the stripplot where the target, `tradeMoney`, has obviously lower than 5000 when 'rentType' is 'shared_rent'?

I am dealing with a house prediction problem. When I am doing EDAs I find the such stripplot() as follows: ...
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Difference between three equivalent ResNeXt blocks

https://arxiv.org/abs/1611.05431 I have been reading this article and have a question about the following three equivalent ResNeXt blocks. In the article, it says Under this simplified case, ...
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We know the subspace generated from the data instances, but we cannot constitute the origin space

I was wondering, what if we know the subspace generated F from the data instances, but we cannot constitute the origin space E that can be in higher dimension, and can easily lead us to the true join ...
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How to choose feature which is used to fill another feature's missng values?

I am dealing with a house prediction problem. However, it has about 10% missing values in buildYear which is one of the most important features. I tried filling ...
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how to deal with two high correlations feature which both has a low correlation with target

I am doing a prediction of house trade money. Here is the correlation matrix of features whose correlations are larger than 0.3 as follows: ...
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When to create ranges for numeric feature?

For example, in the Titanic Dataset, I'm trying to deal with the numeric datasets, FamilySize and Ticket (Ticket Price). From the many solutions I've seen, a lot of people create ranges for ...
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convert significant features to a set of rules or information

Is there any way to set up some rules from features in a classification model? Assume that we want to classify an employee as someone who will be terminated or not. We found that average hourly pay ...
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Predicting U.S. suicide count based on a set of inputs

I'm trying to design a model (or multiple) that can predict the number of U.S. suicides for a future year, based on a few inputs--"age", "sex", "population" (of the age/sex), and "gdp_per_year". I'm ...
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How to choose an optimal threshold for binary discretization

We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted. But I am wondering except ...
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How to encode a job description for machine learning

I'm working on a sample project and one of the features is the job description of a person (categorical, for example: blue-collar, retired, unknown, unemployed, student, etc.). Since in the future ...
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What are the audio features to best describe a music?

I'm working on the content-based filtering part of a recommender system for an audio streaming project. I firstly used the k-mean algorithm with music genres and one-hot encoding to classify musics ...
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1answer
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How to combine features with different temporal scale in machine learning

We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another ...
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193 views

Combining Latitude/Longitude position into single feature

I have been playing with 2 dimensional machine learning using pandas (Trying to do something like this: https://github.com/freeman-lab/spark-ml-streaming), and I'd like to combine Lat/Long into a ...
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1answer
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Model for Differing Number of Rows per Observation

Looking to build a response model (click or no click) on marketing data which displays varying number of offers to a person. I don't want to model which offer they click but do they click any of the ...
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if new feature downgrade the score for xgboost what do I have to look at?

let say I'm predicting the housing price of Boston(kaggle). if I got some score x then I added new feature y_K if this new feature drop the score. what is wrong with this feature and what do I ...
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129 views

method and dataset for credit card fraud detection

I am trying to create a machine learning model to detect credit card fraud (In our definition, fraud means chargeback). I am kinda stuck now with the dataset that I have. I don't have information on ...
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559 views

Categorical vs continuous feature selection/engineering

I'm working with a dataset with a number of potential predictors like : Age : continuous Number of children : discrete and numerical Marital Situation : Categorical ( Married/Single/Divorced.. ) ...
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1answer
142 views

Feature engineering suggestion required

I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. ...