Random Forests Feature Selection on Time Series Data The Next CEO of Stack Overflow2019 Community Moderator ElectionFeature selection using feature importances in random forests with scikit-learnFeature selection for gene expression datasetFeature Selection for K Nearest Neighbour and Decision TreesOrange 3 - Feature selection / importanceDetermining Important Atrributes with Feature SelectionHow to use isolation forest from sklearn to return the positions of anomalies?Multiple time-series predictions with Random Forests (in Python)LSTM Feature selection processFeature selection for time series predictionMultivariate Time Series Binary Classification
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Random Forests Feature Selection on Time Series Data
The Next CEO of Stack Overflow2019 Community Moderator ElectionFeature selection using feature importances in random forests with scikit-learnFeature selection for gene expression datasetFeature Selection for K Nearest Neighbour and Decision TreesOrange 3 - Feature selection / importanceDetermining Important Atrributes with Feature SelectionHow to use isolation forest from sklearn to return the positions of anomalies?Multiple time-series predictions with Random Forests (in Python)LSTM Feature selection processFeature selection for time series predictionMultivariate Time Series Binary Classification
$begingroup$
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. In one sample I have 500 instances (rows in a table), for each feature. In another sample, I got other 500 instances, and a class.
I'd like to select a subset of the features automatically with the Random Forests algorithm. The problem is that the algorithm (I'm using ScikitLearn, RandomForestClassifier), accepts a matrix (2D array) as X input, of size [N_samples, N_features]. If I give the array as it is, that is a vector (len 500) for the feature x, another (len 500) for the feature y, etc., I get a N_samples x N_features x 500 array, which is incompatible with the requirements of RandomForestClassifier.
I tried to unroll the matrix in a vector, like having so 500 x N_features array, but in that way, in the reduction, it considers all the elements independent feature, and breaks my structure.
How can I reduce the features (by selection) (possibly using this algorithm, but open to other libraries and/or algorithms) keeping the time instances consistent?
My goal is to do classification, so forecasting resources are limitedly useful to me. Also I have the requirement that each sample has those occurrences, and I don't have them as separate samples unfortunately.
python scikit-learn time-series feature-selection random-forest
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
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. In one sample I have 500 instances (rows in a table), for each feature. In another sample, I got other 500 instances, and a class.
I'd like to select a subset of the features automatically with the Random Forests algorithm. The problem is that the algorithm (I'm using ScikitLearn, RandomForestClassifier), accepts a matrix (2D array) as X input, of size [N_samples, N_features]. If I give the array as it is, that is a vector (len 500) for the feature x, another (len 500) for the feature y, etc., I get a N_samples x N_features x 500 array, which is incompatible with the requirements of RandomForestClassifier.
I tried to unroll the matrix in a vector, like having so 500 x N_features array, but in that way, in the reduction, it considers all the elements independent feature, and breaks my structure.
How can I reduce the features (by selection) (possibly using this algorithm, but open to other libraries and/or algorithms) keeping the time instances consistent?
My goal is to do classification, so forecasting resources are limitedly useful to me. Also I have the requirement that each sample has those occurrences, and I don't have them as separate samples unfortunately.
python scikit-learn time-series feature-selection random-forest
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago
add a comment |
$begingroup$
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. In one sample I have 500 instances (rows in a table), for each feature. In another sample, I got other 500 instances, and a class.
I'd like to select a subset of the features automatically with the Random Forests algorithm. The problem is that the algorithm (I'm using ScikitLearn, RandomForestClassifier), accepts a matrix (2D array) as X input, of size [N_samples, N_features]. If I give the array as it is, that is a vector (len 500) for the feature x, another (len 500) for the feature y, etc., I get a N_samples x N_features x 500 array, which is incompatible with the requirements of RandomForestClassifier.
I tried to unroll the matrix in a vector, like having so 500 x N_features array, but in that way, in the reduction, it considers all the elements independent feature, and breaks my structure.
How can I reduce the features (by selection) (possibly using this algorithm, but open to other libraries and/or algorithms) keeping the time instances consistent?
My goal is to do classification, so forecasting resources are limitedly useful to me. Also I have the requirement that each sample has those occurrences, and I don't have them as separate samples unfortunately.
python scikit-learn time-series feature-selection random-forest
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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. In one sample I have 500 instances (rows in a table), for each feature. In another sample, I got other 500 instances, and a class.
I'd like to select a subset of the features automatically with the Random Forests algorithm. The problem is that the algorithm (I'm using ScikitLearn, RandomForestClassifier), accepts a matrix (2D array) as X input, of size [N_samples, N_features]. If I give the array as it is, that is a vector (len 500) for the feature x, another (len 500) for the feature y, etc., I get a N_samples x N_features x 500 array, which is incompatible with the requirements of RandomForestClassifier.
I tried to unroll the matrix in a vector, like having so 500 x N_features array, but in that way, in the reduction, it considers all the elements independent feature, and breaks my structure.
How can I reduce the features (by selection) (possibly using this algorithm, but open to other libraries and/or algorithms) keeping the time instances consistent?
My goal is to do classification, so forecasting resources are limitedly useful to me. Also I have the requirement that each sample has those occurrences, and I don't have them as separate samples unfortunately.
python scikit-learn time-series feature-selection random-forest
python scikit-learn time-series feature-selection random-forest
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 mins ago
user1714647user1714647
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 mins ago
user1714647user1714647
101
asked 18 mins ago
user1714647user1714647
101
101
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user1714647 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago
add a comment |
$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago
$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago
$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago
add a comment |
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$begingroup$
Welcome to this site! If you want to treat 500 values per feature as "all or nothing", i.e. not breaking the structure, one way is to use the average for each feature thus reducing 500 to 1.
$endgroup$
– Esmailian
6 mins ago