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.

Filter by
Sorted by
Tagged with
2
votes
2answers
60 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 ...
4
votes
2answers
139 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 ...
2
votes
0answers
27 views

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 ...
1
vote
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 ...
0
votes
0answers
49 views

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 ...
2
votes
1answer
45 views

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....
0
votes
0answers
22 views

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 ...
3
votes
1answer
73 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 ...
0
votes
0answers
21 views

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 ...
1
vote
2answers
282 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 ...
0
votes
0answers
23 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The data set looks ...
2
votes
0answers
22 views

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 ...
2
votes
0answers
27 views

How to feed key-value features (aggregated data) to LSTM?

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