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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Non constant Feature Importance
I have a financial dataset which has 10 years worth of data. The aim is to build a regressor capable of predicting next year sales. So, if I want to predict sales for 2024, I could use data from 2023, ...
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Feature engineering - filter out columns based on linear correlation result
Besides the other factors (such as domain knowledge), is there any rule of thumb or best practices to keep/remove features that were identified with high correlation between each other?
The problem is,...
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Could converting features into PCs, where #PCs = #features, improve model performance?
Let's say I have a dataset with some number of features (e.g., 10) and a target variable.
I create 10 PCs from the dataset excluding the target variable. Then, I run a few classification algorithms (...
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Can we generate labels for an unlabelled dataset by doing some feature engineering?
I am very new to ML and currently, I am working on building a model that can predict recurring blood donors (a classification problem). I have a dataset which consists of 25 features (gender, height, ...
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permutation importance shuffling
When calculating permutation importance for a certain feature, that feature is shuffled randomly and predictions with the shuffled feature are compared to predictions with the feature in its original ...
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How can I normalize features while preserving information about the original values?
I am trying to feed a neural network stock prices of an instrument. To ensure training stability, I normalize the inputs to have mean=0 and std=1.
However, I thought that for stock prices, the ...
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Feature Extraction for timeseries temperature signal [closed]
i have two temperature signals from which one is sensitive toward a specific event. I would like to know what other features can be useful to extract apart from:
Angles (between the two).
Slopes ( ...
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Best feature engineering approach for interest-based age classification
I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this
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Features for a Content-Based recommendation system
I'm working on a hybrid recommendation system (collaborative and content-based) for an online ordering/shopping app. So far I've managed to identify a data-source for the collaborative model (likely ...
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Understanding the features given in Example 13.1 of Sutton and Barto
I'm struggling to understand the notation used to represent the features within Example 13.1 (Short corridor with switched actions" in the Sutton and Barto RL book.
I assume as it is a free pdf ...
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Feature Engineering on transactional dataset clustering
I have a data set with transactions details from different business (roughly 1 thousand business entities). Each row is a transaction. The structure of the dataset is as follows:
client_id
Sex
Age
...
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How to handle list features in clustering?
I have a dataset where one of the features is a list.
Example:
...
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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 ...
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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-...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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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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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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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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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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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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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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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How to deal with Unix timestamps features of sequences, which will be classified with RNNs?
I want to use RNN for classifying whole sequences of events, generated by website visitors. Each event has some categorical properties and a Unix timestamp:
...
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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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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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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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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 ...