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Questions tagged [feature-selection]

Methods and principles of selecting a subset of attributes for use in further modelling

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1answer
15 views

Model for Differing Number of Rows per Observation

Looking to build a response model (click or no click) on marketing data which displays varying number of offers to a person. I don't want to model which offer they click but do they click any of the ...
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0answers
11 views

if new feature downgrade the score for xgboost what do I have to look at?

let say I'm predicting the housing price of Boston(kaggle). if I got some score x then I added new feature y_K if this new feature drop the score. what is wrong with this feature and what do I ...
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1answer
17 views

xgboost and linear regression new feature analysis

For linear regression, seems like a new feature has to be a linear relation with the target variable. But If you make the new feature for the Xgboost, what do you have to consider to make a new ...
3
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5answers
285 views

Categorical vs continuous feature selection/engineering

I'm working with a dataset with a number of potential predictors like : Age : continuous Number of children : discrete and numerical Marital Situation : Categorical ( Married/Single/Divorced.. ) ...
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1answer
28 views

Having averaged trials which are less than the number of features

Suppose I have an experiment where I have 70 features and 48 samples. The target variable is binary (0,1) and the 48 samples are divided such that 24 of them correspond to outcome 1 and the other 24 ...
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0answers
17 views

Nested cross validation in combination with filter based feature selection

So I have come across this paper that has defined nested cross validation as follows: "Further, when one needs to use CV both for parameter selection (including feature selection) and for estimating ...
6
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3answers
205 views

Regression vs Random Forest - Combination of features

I had a discussion with a friend and we were talking about the advantages of random forest over linear regression. At some point, my friend said that one of the advantages of the random forest over ...
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2answers
32 views

Random Forests Feature Selection on Time Series Data

I have a dataset with N amount of features, each one with 500 instances in time. Let's say that I have for example, the features x, y, v_x, v_y, a_x, a_y, j_x, j_y....
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0answers
8 views

SKlearn Univariate feature selection if the features are continuous and output is categorical

I have a dataset with 200 numerical features, but the target is binary (0 or 1). If I use uni-variate feature selection, What is the right scoring parameter (f_regression or f_classif /chi square)? ...
0
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1answer
54 views

Feature selection - SelectKBest sklearn

I would like to ask how to set paramater k in function SelectKBest for feature selection. I have now around 2300 features, so I think that default value 10 is not enough. Is there any approach, how ...
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0answers
12 views

MongoDB Id as feature for Model

I have several ids as features for my model. These ids are mongodb objectId. How should i process these? for example if ids are finite numeric then its easier to normalise. Since mongodb objectids are ...
0
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1answer
9 views

Score Columns in Azure ML Studio

So I have a data set I have successfully used to train a model, and have decent results. I am using a Two Class Boosted Decision tree for a Boolean output. So far so good. I now want to analyze each ...
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3answers
34 views

Machine Learning, Imputing values that should be blank

Sometimes data sets contain variables that indicate the presence of an event and the value that represented the event. As an example say a teacher wants to predict the grades of his students. Some of ...
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1answer
16 views

Correlation between Time Series Indicators ( Stock Prices )

I am new to time series analysis and I am currently tackling a stock market prediction problem. I have a set of market indicators (such as Bollinger Bands, ADX etc) which are derived from the time ...
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0answers
36 views

Unequal number of features in train and test data

After one hot encoding, I have different number of train and test features. Does it matter if I add extra column with all zero values, so that the number of features become equal in both train and ...
0
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1answer
34 views

How exactly do I extract the important features from strings for machine learning?

Forgive me for my ignorance. Linked below is an image of my dataset with 1000 tuples. https://i.stack.imgur.com/WHIlx.png I have the following questions (1) How exactly do I go about extracting ...
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2answers
81 views

Manual feature engineering based on the output

So, I'm working on a ML model that would have as potential predictors : age , a code for his city , his social status ( married / single and so on ) , number of his children and the output signed ...
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0answers
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Difference between Information Gain and Mutual Information for feature selection

What is the difference between information gain and mutual information? At this point, I understand that information gain is calculated between a random variable and target class for classification ...
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1answer
27 views

Collinearity and Multicollinearity in the features?

What are some advanced or basic methods most used by data scientists/ML Engineers to detect collinearity (or) multicollinearity between features?
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2answers
57 views

Including identifier in machine learning model as feature vs separate model for every identifier

I am new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature. I know it will be bad idea to pit id(...
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2answers
73 views

How do Bayesian methods do automatic feature selection?

Someone asked me this question and I do not know I answered it correctly. I answered the question in the following way: One type of Bayesian method is Bayesian inference and feature selection has to ...
0
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1answer
23 views

LDA as a dimensionality reducer [closed]

I know how to use LDA as a classifier. But how to use Linear Discriminant Analysis as a dimensionality reducer to reduce the number of features and apply logistic regression on top of it. I am using R ...
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0answers
11 views

Regression variable colums (not all measures available)?

I have a dataset it looks this way X = [location, measure_type, value] y = [target_value] The goal is to predict the target_value. It is the same for each location. So for example for location "A" ...
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1answer
14 views

Inverse Binary Feature

I am feeding a binary value into my NN which represents whether the given example is a public holiday or not. Is there a difference between assigning a 0 to public holidays and 1 to all other days or ...
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1answer
26 views

Hierarchical Clustering and Variable Selection

I am using "Single linkage" hierarchical algorithm to cluster my data points with Gower Distance as my data have both qualitative and quantitative variables. After applying this for the full ...
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0answers
28 views

XGBoost feature significance and feature importance

In a regression model it is possible to judge at a specified significance level (often alpha = 5%) whether a variable has a significant influence on the target attribute. With XGBoost, you can use ...
0
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1answer
56 views

How do I create a feature vector for the training of an SVM?

I have an understanding problem with implementing an SVM as a classifier for images. The whole thing should be done in python. Now, when I have extracted all the features, e.g. HOG, contours, textures,...
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1answer
33 views

Dealing with correlated features when calculating permutation importance

I have implemented the permutation importance calculation as found here in my attempt to identify features that contribute little to my model's (Gradient Boosted Tree model) predictive power. The ...
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0answers
21 views

Feature Extraction [closed]

I've a data set for different sensor values(like voltage, pressure, vibration etc) for a machine. I need to extract features from it to be able to do predictive maintenance. Which features should i ...
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1answer
22 views

Features impacting individual predictions after training a classification model

I know my question might look odd but I just wanted to get some insights. Every prediction model will give us predictions for validation data set and it also can give/rank features based on their ...
1
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1answer
29 views

Boruta Python No feature Selected

I run Boruta with RandomForestClassifier the previous day on my data (nb features = 36) and got 17/36 confirmed. Now I run it again and there is 0/36 and stop at the 9th iteration. Any idea why this ...
6
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4answers
86 views

How to handle features which are not always available?

I have a feature in my feature vector that is not always available respectively sometimes (for some samples) it makes no sense to use it. I feed a sklearn MLPClassifier with this feature vector. Does ...
0
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1answer
71 views

ML: How to think feature selection?

What is the basic philosophy behind feature selection and modelling? How do you actually start? Could you please share your real (practical) inputs? Bit of background: I am actually trying to analyse ...
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0answers
21 views

Feature selection using a filter for multiclass problem: What if many features are strongly predictive of few classes?

I'm doing text classification with a bit more than 100 classes. First, I would like to do feature selection by using a filter approach (mutual information or chi2). I planned on using ...
6
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1answer
81 views

Will unnecessary features harm the tree based model?

Is it necessary to drop noisy features (eg column of random numbers) from tree features? I think it's not. sometimes it may benefit but will never cause any harm to the model. Because at each split ...
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0answers
7 views

Provide optional confidence level as an input to the neural network

I have a name, gender labeled dataset and I know the frequency of particular name can occurred in the dataset. I want to develop a neural network which predict gender when given the name as an input. ...
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0answers
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What is serialization? [closed]

A fictional Broadway show has 3 shows every Saturday. Tickets are valid for a particular show and enumerated seat. The process of encoding the showtime and seat number is a unique. Is the process ...
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0answers
22 views

Feature selection with information gain (KL divergence) and mutual information yields different results

I'm comparing different techniques for feature selection / feature ranking. Two of the techniques under scrutiny are the mutual information (MI) and the information gain (IG) as used in decision trees,...
0
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1answer
58 views

How to select variables based on the mean correlation in a correlation matrix?

I have a set of independent variables and I am calculating the correlation matrix between them using the Pearson Correlation Coefficient in Python. A part of the matrix looks like this: From this ...
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3answers
58 views

In linear regression, is there anything I can do if the coefficient for one of the features is unrealistic/inappropriate?

I'm building a simple linear regression model that predicts Home Price using Square Footage, Number of Bed(s), and Number of Bathroom(s). After creating the model, I noticed that the coefficients for ...
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0answers
593 views

How to determine feature importance in a neural network?

I have a neural network to solve a time series forecasting problem. It is a sequence-to-sequence neural network and currently it is trained on samples each with ten features. The performance of the ...
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0answers
22 views

Feature selection through Random Forest and Principal Component Analysis

I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 ...
0
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1answer
60 views

Knowing Feature Importance from Sparse Matrix

I was working with a dataset which had a textual column as well as numerical columns, so I used tfidf for textual column and created a sparse matrix, similarly for the numerical features I created a ...
2
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2answers
73 views

How much data to use for feature selection?

Working on my master's thesis, this is a problem I'm unable to find good resources about. I'm working with data of 18 participants, who are either active or passive. Each participant is then ...
4
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1answer
344 views

When should I use StandardScaler and when MinMaxScaler?

I have a feature vector with One-Hot-Encoded features and with continous features. How can I decide now, which data I shall scale with StandardScaler and which data scale with MinMaxScaler? I think I ...
1
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1answer
83 views

Why does Feature Importance change with each iteration of a Decision Tree Classifier?

After applying PCA to reduce the number of features, I am using a DecisionTreeClassifier for a ML problem Additionally I want to compute the feature_importances_. However, with each iteration of the ...
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0answers
46 views

Remove attributes with missing values exceeding a given threshold in WEKA

I imported csv file into WEKA, i have features that have missing value that has missing value percentage of 70% or above, i want to remove these features by weka or also sort that features by missing ...
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0answers
55 views

PCA for unsupervised feature selection [closed]

If I understood correctly, "using results of PCA to select features" (as recommended in this answer) implies visually analysing bi-plots of first two principal components - i.e. the angle between a ...
3
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3answers
88 views

how to evaluate feature quality for decision tree model

Most of the tutorials assume that the features are known before generating the model and give no way to select 'good' feature and to discard 'bad' ones. The naive method is to test the model with new ...