Questions tagged [features]
The features tag has no usage guidance.
68
questions
2
votes
1answer
25 views
Training & Test feature shape is different from number of columns in dataset
I am making a Sequential Neural Network for regression with 3 dense layers which will be trained on a simple dataset. But before I even get to that part of the code to execute the model I am getting a ...
1
vote
0answers
13 views
Can regression algorithms treat multiple input features as a single feature for prediction?
I have a regression problem where I have multiple RSSI values from 3 beacons and I need to predict the x & y coordinates of the mobile object sending the signals based on the RSSI values captured ...
0
votes
1answer
22 views
How to choose feedforward architecture for few number of features but very large instance?
Assume I have 1 million of data instance and each instance contains 100 feature. For each instance, I also have a lable.
The ...
1
vote
1answer
34 views
How to build multiple variable regression having a mix of numerical & categorical features?
There is a need to estimate Annual Average Daily Traffic Volume (AADT).
We have bunch of data about vehicles' speeds during several years. It is noticed that AADT depends on the average number of such ...
0
votes
1answer
34 views
Non-commutative distance formula
I am trying to find a distance formula or a method that can give the non-commutative distance between two points in a feature space.
Suppose there are two movies represented in an R^n feature space. ...
1
vote
1answer
23 views
Integer encoding and weighing when one feature consists of more names [closed]
Hello I am trying to make a content based movie recommendation system and one feature is genre of the movie. I will give an integer number to each genre randomly. However, some movies are of more than ...
0
votes
1answer
47 views
Resampling : My dataset is categorical or numerical?
I have a dataset with 203 variables. Like age>40 (0 -yes, 1-no), gender(0 or 1), used or not 200 types of drugs (one hot encoded into 200 variables), and one target variable (0 or 1). This is an ...
0
votes
1answer
16 views
Using partially defined features in an unified deep learning model
Suppose we have two types of feature A and B. A is defined for all kinds of samples while B is only defined for some of the samples. Here, B is partially defined does not mean B is missing value (such ...
1
vote
1answer
14 views
Structuring extensive medical histories + demographic information for prediciting future medical outcomes
I'm looking for advice structuring extensive medical histories for predicting future outcomes, specifically hospital admissions.
Let's say I want to predict the whether or not someone will be admitted ...
1
vote
0answers
11 views
Latent space for cross domain numerical features
I would like to find the shared latent space between two set of features. I have source and target domain features already extracted from images. I have 4 set of feature vectors for normal and ...
2
votes
0answers
199 views
How SHAP value explains contribution of features for outliers event?
I'm trying to understand and experiment with how the SHAP value can explain behaviour for each outlier events (rows) and how it can be related to shap.force_plot(). ...
0
votes
0answers
29 views
ANOVA Feature Scoring
in order to score features, in ORANGE, using ANOVA scoring, the features should have a normal distribution?
Thank you,
J
1
vote
2answers
66 views
Does binning a time series with pd.qcut (using quantiles) create data leakage?
Let's say I want to predict whether a company will default on it's debt at some point in time (so binary classification) and one of the time series variables I'm using is the "revenue" of ...
2
votes
1answer
135 views
Multi-Feature One-Hot-Encoder with varying amount of feature instances
Let's assume we have data instances like this:
...
1
vote
2answers
82 views
-2
votes
1answer
40 views
combine two features into one [closed]
In an epidemic disease dataset of 3 months, I have a feature (var dt_died) with the death dates of patients (800 people died out of all 12k unique subjects in this dataset, so obviously only dead ...
0
votes
1answer
74 views
Feature importance difference in two similar machine learning models
Situation 1:
I have trained a text classification model (Model 1) which gives me a probability of true class as X. I have also trained a classification model (Model 2) using only the categorical and ...
2
votes
0answers
31 views
Non-Gaussian like distributions - Classifier of source data fails on target data
I ask you for help on a classification problem (classes are represented by the numbers 0,1 and 2). All features are extracted from time series data (fundamental is sinus shape).
I have a source ...
1
vote
0answers
37 views
Self Organising Map with variable length ordered sets of N-grams
I want to preface my question with the highlighted situation I have might not be applicable to kohonen self organising maps (SOM) due to a lack of understanding on my part so I do apologise if that is ...
1
vote
1answer
31 views
distribution difference between image and text
Once for the task of image captioning I've read that, the features extracted from image and text by deep networks are from two different worlds and got different distribution. My question is how is ...
1
vote
1answer
56 views
Neural networks with not-fixed dimension for input and output
I would like to know if it exists a model/method which can deal with input and output of different dimension.
For example, let us say that the maximum number of info we could have is 6 features and 5 ...
1
vote
0answers
20 views
Machine learning on graphs
I'm looking for some method/model to help me with my current problem:
I have a geometry, consisting of points, and eges. For each point I take information about itself and its neighbours. For now I ...
0
votes
1answer
46 views
Terminology in machine learning: exogenous features vs external features
I am currently writing a scientific paper and do not know whether to call some of my input features of my neural network either external or exogenous.
My neural network receives as input features like ...
0
votes
1answer
770 views
grid search result max_features = 'sqrt' in random forest - how to understand
I did a grid search at random forest params. the result of
print(randomforestreg.best_params_)
The result is =
{'max_depth': 28, 'n_estimators': 500 ',max_features'...
0
votes
2answers
19 views
When combined correlation of features decreases
I'm building a machine learning model in Python to predict soccer player values. I'm trying to predict a "player_value" column containing the value of a specific player. Consider a sample of ...
3
votes
3answers
313 views
How to insert two features in a model when a feature only applies to a certain group in the model
I'm building a machine learning model in Python to predict soccer player values. Consider the following feature columns of the dataframe:
...
1
vote
1answer
44 views
Are my features enough?
I am trying to fit a regression model on a non linear data.
The features I have are around 12 and around 800 samples.
With the help of PyCaret, i tried to fit the data on to around 22 model, and then ...
1
vote
1answer
32 views
Imputing features with NA values in classification task
I currently have a dataset where each observation is a person's traffic ticket history over districts.
For each column, which represents a district:
1 represents that a person has received 1+ traffic ...
3
votes
2answers
2k views
Similarity Measure between two feature vectors
I have face identification system with following details:
VGG16 model for feature extraction
512 dimensional feature vector (...
0
votes
1answer
49 views
2
votes
1answer
313 views
How to handle a feature vector that could be variable length?
I would like to train a machine learning model with several features as input as X[] and with one output as Y. For example Every sample has a Data frame like this: ...
1
vote
1answer
65 views
File path encoding to feature
I am trying to find some sort of encoding algorithm that would allow to transform system file paths eg. "c:/users/file1/subfile2/targetfile" into a feature that I could use in machine ...
3
votes
2answers
271 views
Mathematically prove why sparsity leads to model overfitting
With respect to the stackoverflow post here: https://stackoverflow.com/a/59566478/9130959
I can't quite get why the logic stands: when # features increases, the hypothesis space is expanded, leading ...
1
vote
1answer
26 views
How Calculate Effect (percentage) label of the input variables on the output variable by DecisionTreeClassifier
a description problem below.
I have 10 words like X1 , X2 , X3 , ... , X10
and three Label like short , long , hold.
My problem is that how calculate Effect (percentage) label of the input variables ...
1
vote
1answer
16 views
What should you do with attributes that predictive in an interaction?
I am trying to predict results of football games. Some of our attributes only give meaning for a prediction only when they are considered in interaction with another attribute. To illustrate, a team ...
3
votes
0answers
40 views
NN training with repetitive features
I posted the question also on ai.stackexchange but it didn't get any answers so I though I could try here.
Here is a copy paste:
Let's say you are training a NN in a RL setting where the state (i.e. ...
2
votes
1answer
463 views
Getting the positive impacting features using SHAP
I'm attempting to use SHAP to automatically extract feature names that have a positive impact on my regression models. On inspection of the code I see that the bar plot, for example, determines these ...
1
vote
1answer
270 views
Why linear regression feature coefficients become super large?
Introduction
I've implemented linear regression using sklearn and after all calculations I've got results like this:
...
1
vote
0answers
26 views
Multivariate LSTM RNN DNN returning multiple features for forecasting a time series in Python
I am using the latest installation of Keras with Python 3.6 on Linux Mint with a NVIDIA (NVDA) 2070 GPU.
I am looking up. How to get the return values of my data? How do I use all of the features, and ...
-1
votes
1answer
27 views
How to select the best features for Support Vector Classification
I have a feature set that contains approximately 2 dozen features of technical analysis indicators. My own domain knowledge tells me that some of these features are better than others for predicitive ...
1
vote
0answers
19 views
Feature engineering one step at a time or in bunches?
Currently, I'm working on my very first classification project. If you want to know what dataset I'm working with, think "playing stairway to heaven in your local guitar store", and it will probably ...
1
vote
0answers
24 views
Which outlier detection algorithms give a breakdown of the contribution from each feature?
I am looking for an algorithm that outputs a breakdown of which features contributed the most towards a data point being labelled as an outlier.
It can be supervised or unsupervised.
At the moment, ...
1
vote
0answers
175 views
Keras most important features for text classification
I am working on a problem where I need to classify phrases in one of the two categories (let's A & B). I used the Keras SepCNN model (similar to this) for that and it is giving me some results.
...
7
votes
1answer
7k views
How to get feature importance from a keras deep learning model?
In case of scikit-learn's models, we can get feature importance using the relevant attributes of the model.
I've been working on a RNN, using LSTMs for text embedding.
Is there any way to get ...
0
votes
1answer
213 views
order of features importance after make_column_transformer and pipeline
I have a data preparation and model fitting pipeline that takes a dataframe (X_trn) and uses the ‘make_column_transformer’ and ‘Pipeline’ functions in sklearn to prepare the data and fit XGBRegressor.
...
2
votes
3answers
62 views
If a categorical feature only occurs a few times in a data set, should I drop it?
I have a data set of mostly categorical variables. When I one-hot encoded them some of the features occur less than 3% of the time.
For instance the Tech-support feature only occurs 928 times in a ...
0
votes
2answers
142 views
Can I use more features for my training data than my test data will supply?
I am pretty new to the data science game so pardon me, if the answer to my question should be a no-brainer.
We are looking at manufacturing / quality data where products are labeled 'okay' or 'not ...
1
vote
2answers
221 views
model with features of different sizes
I want to train a model (either classification or regression, doesn't matter) with features/inputs of different sizes, but I am not sure how to do it.
For example, for each data-point, feature 1 and ...
-1
votes
1answer
26 views
what features can I get from the sample?
I have dataset of 100 000 words labeled by surname(is last name / not last name)
Example:
kitchen | 0
kennedy | 1
etc.
I tried extract lenth of word, count of each letter and such simple features ...
0
votes
2answers
203 views
What's the meaning of precomputed features?
When i learn about deep learning, I found dataset with precomputed features form. Link (http://cs.stanford.edu/people/karpathy/deepimagesent/coco.zip). What's the different with usual dataset?
