Questions tagged [feature-engineering]
the process of using domain knowledge of the data to create features that improve machine learning algorithms
294
questions
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13 views
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
21 views
Feature engineering - house price prediction (small dataset) [on hold]
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
17 views
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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6 views
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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12 views
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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1answer
15 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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0answers
12 views
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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1answer
30 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
26 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 ...
2
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2answers
45 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 ...
2
votes
1answer
58 views
Feature selection is not that useful?
I've been doing a few DataScience competitions now, and i'm noticing something quite odd and frustrating to me. Why is it frustrating? Because , in theory, when you read about datascience it's all ...
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1answer
18 views
Adding time as a feature with xgboost/random forests
I am trying to use xgboost for performing some regression and the features I have are rather simple and limited. I have the time stamp associated with some ...
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0answers
13 views
Should I convert noncontinuous numerical values to categorical features?
I'm working with very sparse matrixes and have several noncontinuous numerical fields. Are these values better utilized as continuous (prevent from increasing sparseness) or as categorical features?
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0answers
19 views
Deep learning, signal processing and feature engineering
I have a signals represented in python in dense matrices (the values are y-coordinates from a chart - eg. weather temp etc. in different locations around the world).
I'm currently trying to process/...
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0answers
19 views
How can I train a machine learning model with below characterstics? [closed]
Hi I have a classifier model to solve, which has close to 56k samples and 30 features which ...
0
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1answer
47 views
xgboost feature selection and feature importance
when you create the new feature for data analysis
for linear regression, it is clear that the feature has to be linear with other features is better
but for xgboost what is the guideline to make a ...
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0answers
48 views
Mean encoding for linear regression: leveraging domain expertise
I'm trying to build a linear model to predict a customer satisfaction score that measures the overall store experience. My customer could interact with my store using an offline channel (physical) or ...
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3answers
48 views
Any way to encode IP address efficiently?
The reason is I want to analyze behavior from different IP address.
I want to collect users who connects to my server and put their wanted files to SSD. So I have ip address and user name to analyze.
...
1
vote
1answer
20 views
Filling created feature with values
I'm trying to improve accuracy. I created a few new features based on old features. So I need to fill new feature's empty cell with same values in order to equaling shapes.Then, I tried it with median ...
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0answers
36 views
Can you do automated feature engineering in R?
Since now Python has its own deep feature synthesis library, is there anything similar available in R? I know of bounceR but I'm not sure if it does the job as DFS does. Is anyone aware of anything ...
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1answer
26 views
What should be the criteria to select features using correlation factors between features? [duplicate]
For the titanic dataset, I have done some feature engineering (one-hot encoded the features) and now I have developed a heatmap to view the correlation between different features.
I'm not able to ...
1
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1answer
52 views
To One-Hot-Encode or not to One-Hot-Encode?
I have been struggling to find proof for that but I couldnt
Every time I prepare dataset I face the same issue
when a column is a classification such as ...
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0answers
18 views
Time Series Regression and Exponentially weighted mean of lag values-For Advanced Time Series Experts
I need help for a time series regression problem in engineering features.
Background: The dataset has weekly data for sales/orders of hundreds of products for last 145 weeks totalling 450000 ...
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0answers
19 views
Feature selection for circular data in time-series
I'm predicting ozone concentration based on meteorological and seasonal variables. In the feature engineering stage I converted the MONTH, DAY_OF_WEEK, DAY_OF_YEAR to its sin and cosine components ...
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0answers
12 views
What are the common feature extraction technic to compare 2 sequence of timestamps?
I am building some predictive models for an online shopping site. I have timestamp log of customers' arrival time, time spent on a product's webpage, purchased or not, and a few others.
I have tried ...
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1answer
13 views
How to include both origin and destination in your features?
I'm trying to predict the price of transportation for trucking freight. Two important features that I think would be of great impact are Origin and Destination. What's the best way to include that in ...
1
vote
1answer
26 views
For a regression model, can you transform all your features to linear to make a better prediction?
I was thinking. Would it be a good approach to check your features one by one (assuming you have a manageable amount of them) and see the relationship they have with your target variable, if they ...
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1answer
33 views
Feature engineering for time series (audio signals)
I have a task to perform classification of audio signals using any suitable algorimth. After some research I found out, that CNN from this paper shows promising results. However, it still needs to be ...
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0answers
11 views
Can we vectorize nominal text feature using tfidf or count vectorizer?
I recently participated in the hackathon. The dataset includes drug name, sentiments about the drug, unique id. The target variable is the sentiment.
It was a sentiment classification or analysis ...
2
votes
1answer
45 views
Blind feature engineering
I received a dataset for analysis that had ~100 numeric columns with anonymous column names(X1, X2, X3, etc...) and asked to do a binary classification. My resulting classification algorithm using a ...
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0answers
28 views
How to time series forecast with multiple time series data sets on the same time series index
How does time series work with multiple time series data sets on the same index?
For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
2
votes
2answers
42 views
Training vs test data set for supervised learning in real life scenario
In the tutorials, I have noticed only similar data has been used with models training and prediction.
I was wondering how cases where you can't find training data that is similar to your final use ...
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3answers
47 views
Can we use median to replace all the missing values from a column
If we can use the median to replace all of the missing values from a column, then what is the advantage of using median over mean for replacing the missing values in a column with numeric values?
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1answer
18 views
Using strong predictor in Model training?
I am trying to build a Disease predictor based on symptoms. I am using data scraped from Symcat website. After sampling the data we have symptoms to disease mapped for training purpose.Data looks like ...
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9 views
What kind of feature engineering suits for random generation like time series data
My time series data looks like random generation which was obtained by aggregation day-wise sales of a product. The dependent variable does not show any kind of pattern.
So have tried tries different ...
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1answer
24 views
Reduction of feature values
I have a data set of 700+ mil records with a feature that should yield good predictive power. The problem is that it has far more unique values than it should. The 10k+ unique values should map to ...
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1answer
54 views
Depending samples in ad ranking and click rate prediction
I am struggling with the following problem:
Suppose we fit a machine learning model to model advertisers click rates. I used a Logistic Regression approach using a one-hot/dummy encoding.
We have ...
1
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1answer
32 views
Feature selection method explanation
In the context of feature relevance, I am trying to understand the meaning of the correlation method for feature selection. Can somebody please explain if the following results of the correlation ...
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0answers
27 views
Working with a biased sample of the data
I have a project to build an automatic fraud detection system for an energy company. I have a huge amount of unlabeled data and only an small and biased labeled one. My labeled data is based on ...
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0answers
18 views
What happens if you add a constant value to all input data points to neural networks?
I have a somewhat basic question about neural networks. What would be the effect on the performance of a neural network if we add a constant value to all data points? For example, suppose you have ...
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0answers
14 views
How to determine best lagged value for feature engineering sales data forecasting
Am trying to predict daily sales of a retail product, as part of data setup have extracted sales day, dow, a year from sale date.
I would like to apply XGBOOST algorithm on the sales pre-processed ...
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0answers
48 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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0answers
10 views
How to do feature engineering on this situation that the almost all label of high count part is 1
I get a feature-target analysis as follows：
...
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0answers
30 views
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 ...
0
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0answers
19 views
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 ...
2
votes
1answer
172 views
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 ...
1
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1answer
17 views
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-...
1
vote
1answer
35 views
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....
3
votes
1answer
63 views
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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0answers
17 views
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
...
