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
0 votes
1 answer
20 views

How to handle list features in clustering?

I have a dataset where one of the features is a list. Example: ...
user avatar
0 votes
1 answer
27 views

How can I vectorize fictional single word (not sentence!) for classification?

I am working on fictional single words (names) generator that have to sound like words from a given sample. I have the generator up and running that gives reasonable words 70% of time. I thought of ...
user avatar
  • 101
0 votes
0 answers
14 views

Travel time between locations, two features with locations or one feature with segment

I'm working on a project where we are using supervised machine learning to predict the travel time between locations. The output value is the travel time. I'm debating whether to have two features or ...
user avatar
0 votes
1 answer
31 views

Sensible integer embedding/encoding for distinguishing elements of a set?

I am trying to train a model that takes in a set of feature vectors (which comes with an ID to uniquely identify elements of the set) and outputs a target for each element in the set (in a permutation-...
user avatar
  • 101
1 vote
1 answer
44 views

Are derived or computed inputs bad for CNNs?

I am building a CNN and am wondering if inputting derived or computed inputs are generally bad for the effectiveness of CNNs? Or just NNs in general? By derived or computed values I mean data that is ...
user avatar
2 votes
0 answers
19 views

Is it a good practice to split sparse from dense features?

I have a mixture of real (float) and categorical features to use as input in a neural network. I encode the categorical features using one-hot / multi-hot encoding. If I want to use all the features ...
user avatar
  • 59
0 votes
1 answer
47 views

Generating a dataset from data with "assumed" lables

I've got a task similar to the following: Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those ...
user avatar
  • 103
0 votes
0 answers
24 views

Feature Descriptor for Range Doppler Maps

I would like to know which is the best feature descriptor for a sequence of Range Doppler Maps: I have some suggestions like hog features, haar features, SIFT and SURF. Any other suggestion? For the ...
user avatar
2 votes
1 answer
47 views

What can be an example for the prior knowledge used in Deep Learning systems?

It is known that machine learning algorithms expect feature engineering as an initial step. Now, consider the following paragraph, taken from 1.1 The deep learning revolution of the textbook named ...
user avatar
  • 2,977
1 vote
0 answers
34 views

How to pass variable length data as feature to a neural network?

I am working on building a model to classify the type of touch the user makes(Long Press, Left Swipe, Right swipe and so on). I have data with features that characterise the user's touch, like ...
user avatar
0 votes
0 answers
46 views

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 ...
user avatar
2 votes
1 answer
223 views

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 ...
user avatar
  • 1,324
1 vote
1 answer
444 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 ...
user avatar
0 votes
0 answers
25 views

(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, ...
user avatar
  • 753
0 votes
0 answers
32 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 ...
user avatar
  • 101
1 vote
1 answer
51 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 ...
user avatar
  • 13
2 votes
2 answers
92 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 ...
user avatar
  • 489
4 votes
2 answers
201 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 ...
user avatar
  • 247
2 votes
0 answers
32 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 ...
user avatar
  • 121
1 vote
1 answer
34 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 ...
user avatar
3 votes
1 answer
56 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....
user avatar
3 votes
1 answer
83 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 ...
user avatar
0 votes
0 answers
24 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 ...
user avatar
  • 143
1 vote
2 answers
1k 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 ...
user avatar
2 votes
0 answers
25 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 ...
user avatar
2 votes
0 answers
30 views

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

I have the following time-series aggregated input for an LSTM-based model: ...
user avatar
  • 121
1 vote
1 answer
71 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 ...
user avatar
1 vote
2 answers
118 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 ...
user avatar
  • 1,029