Questions tagged [feature-weighting]
26 questions
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votes
3 answers
How to describe most important features of ensemble model as list?
I have created 3 different models and output of them is a class probability in binary classification problem. Models are bit different, showing importance from different features. I have of course one data matrix as a source for this exercise where…
Analyst
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1 answer
How to find significant predictors that can differentiate case and control without ML approach?
I have a dataset with more than 70 columns and I have an binary output column.
What I did currently was to explore the dataset by plotting the bar and line graphs for the input variables vs output column.
Though I see that certain variables show a…
The Great
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1 answer
Basic question about feature selection
I am new to machine learning.
I have a basic question about feature selection. I have a dataset with 100 features which I used to regress an output Variable. When I do regression with all the features, I get a particular regression error, r1. When I…
Peter Coggle
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1
vote
0 answers
XGBoost and AdaBoostClassifier feature importances
I try to compare XGBoost and AdaBoostClassifier (from sklearn.ensemble) feature importances charts.
From this answer: https://stats.stackexchange.com/a/324418/239354 I get know that AdaBoostClassifier give us feature_importances_ based on gini…
K4liber
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1
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1 answer
On feature scaling and weighting for clustering
The issue of feature scaling and weighting for cluster formation has been widely discussed in several books and papers as well as several questions (e.g. here ). To my understanting, variable range is the one to be considered as the weight of a…
Alex
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vote
0 answers
From unsupervised clustering to a weighted average?
I am in the process of writing my master's thesis, and have stumbled (as one often do..) upon an area of statistics that I am not familiar with. My setup is, that I initially have 2 features which I cluster based on some algorithm, to find groups of…
Philip
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1 answer
Why wider range for a feature in Machine learning affects training?
I was reading through the Google Machine learning crash course and I can't digest the below point:
If a feature set consists of multiple features, then feature scaling provides the following benefits:
Helps the model learn appropriate weights for…
Anu
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1 answer
Why does Feature Scaling work?
If I take a very basic example where my feature Matrix X is
$$
\begin{matrix}
1 & 100 & 0.25\\
1 & 110 & 0.5\\
1 & 120 & 0.75\\
1 & 130 & 1\\
1 & 140 & 1.25\\
\end{matrix}
$$
and the expected output vector Y is…
Prashant
- 113
- 5
1
vote
1 answer
Statistical method(s) to employ to find best features given a number of features
I am working on an anomaly detection application that uses keystroke dynamics.
This is the pool of features that I have to my disposal:
hold time = R(i) - P(i)
key-up to key-down = P(i+1) - R(i)
key-up to key-up = R(i+1) - R(i)
key-down to…
TMK
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vote
0 answers
How to reweight two datasets so they are the same by deleting members?
So my situation is that I have a large set of events, each of which contains many variables (e.g. mass, length, momentum, colour...). This set of events can be divided into two categorys according to the colour variable, all the events are either…
Clumsy cat
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How to justify that the weight of features is significant in Relieff method for feature selection?
I am a bit confused about how to justify the significance of features whose weights are determined by ReliefF method in binary classification problem. Is there any certain value of weight that make some features more significant than the others?
I…
Dziban N
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0
votes
1 answer
Handling features which have the default value in most instances
I am using a Generalized Additive Model to predict a score between 0-100.
One of the features in the model is a boolean value which is rarely true.
When the value is true, it is a very strong signal that the score should be low. When it is false,…
łówkèÿ
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0
votes
0 answers
Give more importance to particular features in Keras
This is related to this other question but focusing on a particular potential solution. Reading the other question might help in understanding the background of this one, although the other is quite longer.
Is there any way in Keras to force the…
Jivan
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0
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Extracting feature weights after fitting SVC with pre-computed linear kernel
I'm using sklearn's SVC with a linear kernel to train and predict brain states from functional MRI data. Upon completion, I want to extract the feature weights to identify which of these contain the most discriminative informaiton. The…
Mark
- 1
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0 answers
Implementing nested features in unsupervised models
Our project has built an unsupervised model that uses data about a number of companies. Some of these companies are public and some are private. The ones that are public have much higher financial reporting requirements than the private companies…
Michael Szczepaniak
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