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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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How to properly select features for time series ML models

I've been trying to get good references on how to solve a problem that's been bothering me regarding the modelling techniques I've used. I'm currently interested in making forecasts using ML for ...
loguimaraes's user avatar
1 vote
1 answer
22 views

Feature Engineering a Recency feature

I have a customer scoring problem I'm working on specifically on predicting conversion and coming up with a probability score on conversion (using xgboost classifier atm). There's a feature I want to ...
MetalicSt33l's user avatar
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1 answer
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how to handle a variable number of feature-values (1:many) without one-hot

I am using Catboost and one thing I notice in the guide is that it says to not preprocess to one-hot encoding. My data has a single target per row however the feature can have both thousands of values ...
tuj's user avatar
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3 votes
1 answer
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Using training data that requires manual interpretation

I have a dataset that comprises several data streams that are measured on objects (>10k objects). The data is essentially time series data (0.5 second intervals). Typically, an expert interpreter ...
user1563247's user avatar
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1 answer
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How do I give weight to recent time points when predicting another closeby time point?

I am building a normal feed-forward neural network to predict the value of a masked time point using regression, e.g. I have values for x at times 1, 2, and 4, and I want to predict its value at time ...
Michel Hijazin's user avatar
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Variable Selection and model prediction

In a supervised problem, I used randomForest for variable selection to identify the most important features. Question: am I required to use a random forest model for subsequent predictions, or can I ...
Zakaria Faouzi's user avatar
0 votes
1 answer
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Missing data in train set and test set

I have a dataset of N columns. Now I'm able to preprocess data and find a subset of features that I can use to train a model and make predictions. In the case where the train data has missing feature ...
0-0's user avatar
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0 answers
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Feature Selection in no labeled data

I'm new to this field and trying to learn by working with a fraud dataset. Initially, I used the dataset as is, but now I'm trying unsupervised learning without the labels. I've tried clustering ...
DrGenius's user avatar
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4 votes
2 answers
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What is the best way to train a neural network with a variable number of inputs?

Suppose I have a neural network with 5 inputs: [A,B,C,D,E] There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are ...
user18959's user avatar
0 votes
1 answer
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Dealing with only categorical features dataset

I'm trying to do multi-class classification on a labeled dataset with purely categorical features. There are around 30 features in total. 3 of the features in particular have around 100 unique values (...
Shaurya Uniyal's user avatar
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0 answers
28 views

Can my LSTM model learn feature engineering on its own?

I have a timeseries dataset and I am training an LSTM model on it to perform multiclass classification. My dataset has 7 columns => x1,x2,x3....x7 And has 4 labels => f1,f2,f3,f4 Since I have ...
Rushabh Kheni's user avatar
0 votes
2 answers
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Time Series forecasting feature creation/engineering

I'm new to time-series-forecasting and was wondering, whether in a single variable forecast e.g.: X -> Y the creation of additional features of X leads to an improvement when training. So if adding ...
user159972's user avatar
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How can I extract useful data out of a molecule?

In my dataframe, I have a feature with molecules like C1=CC=C(C=C1)C(=O)OC2=CC=CC3=C2C=CC=C3O, and C1=CC=C(C=C1)CCCNC(=O)/C(=C/C2=CC(=C(C=C2)O)O)/C#N. I have no experience with RDKit and deepchem, and ...
Tanmay Sharma's user avatar
1 vote
0 answers
41 views

Scaling imbalanced binary features

I am interested in a discussion in encoding and scaling categorical features, notably imbalanced categorical features. The context is neural networks (gbdts should handle this easily). It is known ...
Lucas Morin's user avatar
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1 answer
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How to add lagging Features for Forecasting with a random lag range without adding a new column per lag?

The common way of adding lagging features in time series forecasting problems is adding lag columns with pandas.shift(). While it is a fine method but what about when wanting to use a random integer (...
Emad Ezzeldin's user avatar
-1 votes
1 answer
150 views

Can Machine Learning Algorithms Process Contextual Features for Regression?

Take Figure 1 showing point interpolation, where point L0 is being interpolated using points L2 and L1 and the distances L11, L12, L21, and L22. Whilst the graph shows a linear interpolation example, ...
Emad Ezzeldin's user avatar
0 votes
1 answer
23 views

How to improve the influence of one element of the input on the latent code in an autoencoder?

I am trying to apply an autoencoder for feature extraction with the input like I=[x1,x2,x3,...,xn]. Representing the latent code after encoding as L, I want to improve the influence of one element of ...
JJbow's user avatar
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0 answers
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Improving the performance of gradient boosting classifier

I am training a gradient boosting classifier on an imbalanced data but the model is not performing very well. These are the things I have done to improve the model's performance. Balanced the data ...
Toluwalope Owolabi's user avatar
1 vote
1 answer
52 views

How to normalize the features without the knowledge of the min and max values in online learning?

I am developing an online learning platform where input features are gathered from various sensors. However, these features may have vastly different ranges. For example, displacement values may be ...
JJbow's user avatar
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0 votes
2 answers
54 views

Encode 10k features where each feature is having more than 500 categories

I have around 10k features in my dataset and each feature is having more than 500 categories. what is the best encoding method to convert this categorical features to vector form? "span_dir":...
khushi's user avatar
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Deriving consistency feature for a student using a study app over the days

I want to build a recommendation engine for the revision app. Basic Idea After each module we will ask student questions and based on the correctness of their answers we will decide after how many ...
Keshav Raj's user avatar
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2 answers
407 views

Handling categorical variables for Xgboost?

Currently there seems to be two approaches for handling categorical variables in gbdts: Xgboost as an option, but data need to be encoded properly (integers) Catboost can handle everything provided ...
Lucas Morin's user avatar
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Alternatives to Model-Based Feature Selection for Unsupervised Clustering

I am running a clustering model on a group of patients who are hypertensive with hopes of identifying different variations in clinical characteristics among hypertensive individuals. One of the issues ...
Zory Dory's user avatar
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0 answers
7 views

How to handle inherent missing data in constructed lagging indicators?

I have a dataset of sports statistics from over the course of several seasons, and I want to incorporate some lagging features for a deep learning time series model. Specifically, I want to generate ...
moistnar's user avatar
0 votes
1 answer
64 views

Correct order of preprocessing/EDA/feature engineering?

I was wondering if I have the correct order of preprocessing/EDA/feature engineering below? Yes there are nuances and may vary from problem to problem, but am just looking for a general pipeline for ...
Katsu's user avatar
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0 answers
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propper feature encoding

I working with the following data set also here is it's detailed description of "packet_dat" column I can't understand how I can encode packet_dat column into proper feature so my ...
Roma's user avatar
  • 101
0 votes
1 answer
24 views

Is there a standard data science workflow/decision tree?

I'm looking for some kind of reference that essentially shows an example of an entire data analysis workflow beginning with feature engineering, and ending with analyzing the results. I know the ...
David's user avatar
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1 vote
1 answer
50 views

How do I use ML models to estimate current stress level based on past data?

I am new to machine learning and I cannot understand the difference between estimating current stress level and predicting future stress levels based on historical data. I have been told these are two ...
user123456789's user avatar
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0 answers
19 views

How to lower MSE using polynomial regression?

I have a training dataset with the positions (x and y) of three objects and their velocities at a time t. Then I have a test dataset with the initial positions and a time step x. The goal is to ...
iknownas's user avatar
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0 answers
21 views

Preprocessing overheads in Machine Learning

Meta reports that data preprocessing overheads is fast becoming a bottleneck to machine learning training (https://engineering.fb.com/2022/09/19/ml-applications/data-ingestion-machine-learning-...
Rajath Shashidhara's user avatar
1 vote
1 answer
45 views

Feature selection for propensity model

I'm trying to build a propensity model for whether or not a customer will buy a second product. I was given data that looks like this: | Age | Income | DaysSince1stPurchase | Bought2ndProduct | |:---- ...
BlueSkyz's user avatar
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Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes?

Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes? Please see the following.
DSPinfinity's user avatar
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0 answers
27 views

Incorporate a new feature or Post-process

Briefly, I am training a model using XGBoost to predict future quantity for the factory to produce. Basic features currently in use are date time features, categories, holiday (binary). I have just ...
Bourbon's user avatar
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0 answers
7 views

Terminology: What is it called when the filter criteria is added to the data table?

When we want to focus on a group within our dataset, it is common to filter it. Suppose we have a data table showing how many points 4 players earned in a game. Table 1: Game Scores for 4 Players Age ...
madprogramer's user avatar
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1 answer
63 views

Unclear points on projection type and selection of distance metric in feature extraction for a set of scenarios

The following is an example from a book (An Introduction to Pattern Recognition and Machine Learning by P. Fieguth, page 85) on feature extraction and selection. Please consider the following figure. ...
user154739's user avatar
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0 answers
8 views

How to handle multiple values which have multiple subsets?

I am new to datascience. I am trying to predict tab position in msword using features like paragraph text, font name, tab count, tab index etc. In tab position there may be 0 values or multiple ...
Kamal Budhathoki's user avatar
0 votes
1 answer
35 views

Is this Dataset somehow skewed?

I am working on a dataset that has 100K points, it's about Customer churn. So I don't know whether this dataset is skewed, incomplete or what. I tried doing some feature engineering on it but couldn't ...
Harshal R's user avatar
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0 answers
44 views

One-Hot encoded variables dominates importance among other variables

I am currently training some machine learning models to predict the 28-day compressive strength of cement, a continuous real-valued variable. The available dataset comprises samples from three ...
Felipe's user avatar
  • 11
0 votes
0 answers
8 views

Grouped Time Restricted Demand Regression with value cap

so I am working on quite an interesting regression task that I haven't encountered before. Our company sells products (steel) in tons. We offer contracts where the customer orders a certain amount of ...
Martin Pichler's user avatar
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0 answers
13 views

Optimal method for predicting outcome from many additive, correlated, and sparse features?

Suppose I have many vectors which can take on any of three values, 0, 1, 2. These vectors affect an outcome being predicted, Y. Vectors add together: a vector "A" of the value 2 has twice ...
BigMistake's user avatar
0 votes
1 answer
34 views

Best strategy for handling missing groups of features

I am currently working on a ML problem where the features used for modelling are sourced from different places/providers. It is very unlikely to find the features from all the different sources to be ...
recentadvances's user avatar
0 votes
0 answers
17 views

Sending rolling statistics to RNN

I'm curious if anyone has seen cases where sending rolling statistics such as mean, median, min, max, standard deviation, skewness, kurtosis, etc. have been helpful for model accuracy? If so please ...
noNameTed's user avatar
0 votes
0 answers
17 views

Matrix time-series Forecasting with LSTM

I have the following time-series data: ...
Louis GRIMALDI's user avatar
0 votes
0 answers
67 views

time series analysis: lag features

I plan to include lag features in my multivariate time series data. I have 17 input feature and one output feature. both input and output features are time series. I will insert one more input feature ...
mehmet's user avatar
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0 votes
1 answer
46 views

Natural Order should be maintained while ordinal encoding?

I am encoding my ordinal categorical values as VHigh=1, High=2,Med=3,Low=4. Am I doing correct? or order doesn't impact? If it impacts, how does it impact Decision Tree, Logistic Regression, SVM?
Pramod yadav's user avatar
0 votes
1 answer
44 views

Variable length training data for tabular data neural network regression

I want to predict the age of a parent using the ages of its children. The problem is that in the data each parent has different numbers of children. How do I create a model that can take variable ...
Pibben's user avatar
  • 101
-1 votes
1 answer
23 views

How to Data Engineer a dataset to get the best featurres to predict a target class?

In my dataset, I have data of IDs that don't create any meaningful relationship with each other and when I test that dataset on different models I am not getting accuracy more than 40%. Anyone can ...
Farhan Aslam's user avatar
0 votes
1 answer
398 views

Feature selection / missing values

What are the top (including new, if any) algorithms to perform features selections without removing or altering the missing data points ? Thanks
Zak's user avatar
  • 1
0 votes
1 answer
13 views

feature engineering mechanism

why do we need to rescale some feature having large range I know we do it for faster rate of gradient descent ,but still how does rescaling works? and it doesn't break the model and does rescaling ...
rushi jhala's user avatar
0 votes
0 answers
60 views

Effect of removing duplicate and identical entries on dimensionality reduction

I have huge data with thousands of observations and millions of features. I need to do clustering so I use PCA/t-SNE/UMAP for dimensionality reduction followed by K-Means. Currently, I retain only ...
Quiescent's user avatar
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