Questions tagged [feature-engineering]

For questions related to feature engineering, which is the process of using domain knowledge to extract features from raw data via data mining techniques.

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Does randomly adding hand-engineered features increase the CNN's sample efficiency/performance?

It is a known fact that preprocessing images using CV techniques will improve CNN performance (see this answer). But what happens when you feed in the entire image and the filtered image randomly to ...
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How does a decision tree split a continuous feature?

Decision trees learn by measuring the quality of a split through some function, apply this to all features and you get the best feature to split on. However, with a continuous feature it becomes ...
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1answer
54 views

Feeding CNN FFT of an image, a dumb idea?

My dataset consists of about 40,000 200x200px grayscale images of centered blobs bathed in noise and occasional artifacts like stripes other blobs of different shapes and sizes, fuzzy speckles and so ...
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(Deep) feature engineering for lambda terms (mathematical expressions, higher order logic formulas) - is such thing?

Automated theorem proving with (deep) reinforcement learning (DRL) approach is hot topic in current AI research when domains of games are becoming saturated and completed research topics. For example, ...
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27 views

Do the training and test datasets need to be equally preprocessed as one whole dataset?

I have developed, trained and tested an NLP model. It is persisted in a pickle file. The model contains the data preprocessing function that includes text cleaning and new features engineered with ...
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1answer
40 views

Is it a good practice to pad signal before feature extraction?

Is padding, before feature extraction with VGGish, a good practice? Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in ...
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2answers
61 views

Is feature engineer an important step for a deep learning approach?

I'd like to ask you if feature engineering is an important step for a deep learning approach. By feature engineering I mean some advanced preprocessing steps, such as looking at histogram ...
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2answers
140 views

When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Usually, Neural Networks uses raw data. You do not need to extract features manually. NN's can find & extract good features which is a pattern of an image, signal or any kind of data. When we ...
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How to find good features for a linear function approximation in RL with large discrete state set?

I've recently read much about feature engineering in continuous (uncountable) feature spaces. Now I am interested what methods exist in the setting of large discrete state spaces. For example consider ...
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1answer
26 views

Does feature scaling have any benefits if all features are on the same scale?

By scaling features, we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger ...
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In AlphaZero, which features are one-hot encoded and which are single real-valued?

From the AlphaZero paper, the caption of Table S1 (p. 13) Table S1: Input features used by AlphaZero in Go, Chess and Shogi respectively. The first set of features are repeated for each position in a ...
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Is automated feature engineering a path to general AI?

I recently came across the featuretools package, which facilitates automated feature engineering. Here's an explanation of the package: https://towardsdatascience....
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What is the best way to do classification using both text and numerical data?

For my BSc thesis I am trying to classify asset price direction (up/down/neutral) using numerical features and Swedish text. The text is short financial news (ca 50 words each) that have sentiment ...
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75 views

How to perform prediction when some features have missing values?

Sorry if this is too noob question, I'm just a beginner. I have a data set with companies' info. There are 2 kinds of features: financial (revenue and so on) and general info (like the number of ...
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What are some solutions for dealing with time series data that are recorded at uneven intervals?

Let's say I have a time series data which is a bunch of observations that occur at different time stamps and intervals. For example, my observations come from a camera located at a traffic ...
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2answers
407 views

Why are decision trees and random forests scale invariant?

Feature scaling, in general, is an important stage in the data preprocessing pipeline. Decision Tree and Random Forest algorithms, though, are scale-invariant - i.e. they work fine without feature ...
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Visualisation for Features to Predict Timeseries Data

I have a course assignment to use an LSTM to predict the movement directions of stock prices. One of the things I am asked to do is provide a visualization to compare the predictive powers of a set of ...
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How to feed key-value features (aggregated data) to LSTM?

I have the following time-series aggregated input for an LSTM-based model: ...
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1answer
55 views

Can feature engineering change the selection of the model according to the minimum description length?

The definition of MDL according to these slides is: The minimum description length (MDL) criteria in machine learning says that the best description of the data is given by the model which ...
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3answers
109 views

Can neural networks be used to find features importance?

I am wondering if I can use neural networks to find features importances in similar manner as it can be done for random forests or decision trees and if so, how to do it? I would like to use it on ...