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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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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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1answer
19 views

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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20 views

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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10 views

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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24 views

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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1answer
23 views

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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12 views

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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21 views

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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16 views

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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1answer
23 views

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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2answers
35 views

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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32 views

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
13 views

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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2answers
78 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
40 views

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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48 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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5answers
368 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
135 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. ...
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15 views

Aggregating target-encoded array-like categorical features?

I am trying find commonly used techniques when dealing with high cardinality multi-valued categorical variables for machine learning classification algorithms. One-hot encoding leads to very high ...
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0answers
19 views

How to incorporate an attribute that only exists in some observations?

In a binary classification problem, some of my observations have an event that occurs. I can, obviously, add a 1/0 flag if the event occurs ("event_occurred" in the data below). However, my intuition ...
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1answer
42 views

Do I need to engineer lagged features when creating an LSTM for time series forecasting?

Long short-term memory networks are fairly complicated and I haven't completely wrapped my head around them. It seems to me like the big gain in LSTMs for time series forecasting is the lacking ...
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2answers
33 views

Pros and cons of using the zscore of a dataset before normalizing it during feature engineering?

Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it. I think that would result in losing some of the information ...
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2answers
36 views

Creating a metric based on some features

I want to create a new metric based on some features but dont know how to start. I basically want to create a "job satisfaction level" metric based on some features. The features could be work hours, ...
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3answers
243 views

Regression vs Random Forest - Combination of features

I had a discussion with a friend and we were talking about the advantages of random forest over linear regression. At some point, my friend said that one of the advantages of the random forest over ...
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1answer
37 views

Machine learning algorithm that can use many instances to predict 1 continuous outcome per person

I am trying to use movements identified from accelerometers during sleep to predict gait speed (continuous). I am trying to figure out what the best machine learning algorithms/ feature extraction ...
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1answer
26 views

is it bad to have many different measurements for the same target variable?

I'm working on a dataset that has repeated measurements for the same target variable. When I don't change anything and create model, cross validation overfits with 0.99 score but in testset it gives ...
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31 views

How to deal with a potencially multiple categorical variable

I'm build a model that has, as inputs, some categorical variables. I had already dealt with this sort of data before, and applied different techniques as creation of dummy variables and factor scoring....
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1answer
38 views

How exactly do I extract the important features from strings for machine learning?

Forgive me for my ignorance. Linked below is an image of my dataset with 1000 tuples. https://i.stack.imgur.com/WHIlx.png I have the following questions (1) How exactly do I go about extracting ...
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2answers
44 views

How exactly do I go about extracting features from timestamps for machine learning? [closed]

My dataset has a timestamp column with the following format: 06/24/18 0:56 How exactly do I convert this information into features that can be used for classification algorithms like logistic ...
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2answers
88 views

Manual feature engineering based on the output

So, I'm working on a ML model that would have as potential predictors : age , a code for his city , his social status ( married / single and so on ) , number of his children and the output signed ...
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0answers
57 views

Target Encoding: missing value imputation before or after encoding

I want to perform a target encoding for my categorical features although I am not sure when to perform the data imputation if any of them has missing values. Let's say I have a few continuous features,...
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1answer
69 views

Why is Reward Engineering considered “bad practice” in RL?

Reward engineering is an important part of supervised learning: Coming up with features is difficult, time-consuming, requires expert knowledge. "Applied machine learning" is basically feature ...
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Add features from a timeserie target to train set

I have a one year train set, which is a combination of a sequence of time ordered images, while the target is a continuous variable. The source of the problem is that data-set is very small, ...
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1answer
73 views

Multiclass classification in a balanced dataset with one high-priority label

I have a balanced dataset for a multiclass classification problem with one high-priority label (this ought to be classified properly at all costs). How do I go about creating a workflow for this ...
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1answer
14 views

Inverse Binary Feature

I am feeding a binary value into my NN which represents whether the given example is a public holiday or not. Is there a difference between assigning a 0 to public holidays and 1 to all other days or ...
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2answers
71 views

How to Work with Imbalanced Data

I am building a binary classifier from a set of feature vectors some of which are categorical like Yes or No (two options). I am replacing them with 1 and 0 and since there is strong imbalance between ...
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1answer
201 views

Python Time series: extracting features on a rolling window basis

I have a long univariate time series, and before performing some machine learning models with it, I want to extract as many features as I can from the time series on a rolling-window basis. As a ...
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33 views

Adding Fourier transform features to data

I'm working on some timeseries data which after visualising seems to be periodic(repeating at some interval), So I planned on finding the Fourier transform of the entire and pick the top n amplitudes ...
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1answer
23 views

Feature Engineering Lists\Vectors as values in dataframe

Let's say I have a dataframe where some of the columns have lists of strings as values. I would like to use ML Algorithms on this dataframe. In this case, I can: I could add many columns of 1's and ...
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27 views

Feature encoding for multiple JSON objects

I have a dataset, where a particular feature is a collection of many JSON objects for a single feature. ...
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0answers
28 views

Measure the “aggregate preference” of points on a 2D plane

Consider some points on a 2D plane, we know corrdinates of all points, how to measure the "aggregate preference" of them. I don't know what is the exact terminology of "aggregate preference". What I ...
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1answer
26 views

Should I expect major performance improvements by scaling my features?

I'm trying to decide whether I should scale my features & responses for training, and I'm in a situation where I can't just try both scaling and not scaling. My features currently have an std ...
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1answer
32 views

Problem with important feature having a lot of missing value

I am facing a dilemma with a project of mine. One of the variables (numerical) doesn't have enough data i,e almost 99% data are missing. However, upon talking to the domain experts, it appears that ...
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1answer
75 views

ML: How to think feature selection?

What is the basic philosophy behind feature selection and modelling? How do you actually start? Could you please share your real (practical) inputs? Bit of background: I am actually trying to analyse ...
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7 views

Provide optional confidence level as an input to the neural network

I have a name, gender labeled dataset and I know the frequency of particular name can occurred in the dataset. I want to develop a neural network which predict gender when given the name as an input. ...
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1answer
22 views

Why feature crosses may work better than having them as individual features?

On Google ML Crash Course it is said the following: If we build a feature cross from both these features: [behavior type X time of day] then we'll end up with vastly more predictive ...
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24 views

Feature selection through Random Forest and Principal Component Analysis

I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 ...