All Questions
Tagged with machine-learning feature-engineering
249
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14
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Using LSTMs for Predicting Targets with Known Feature Vector
I am trying to use an LSTM to predict the consecutive "offset" calibration values for an instrument. These offset values have previously been shown to be well correlated with a pair of ...
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32
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How does one handle a dataset with groups of features and groups of labels in classification?
I have a large dataset (1.8mil samples). There are 15 features: x1, y1, z1, e1, d1, x2,..., d3. (x,y,z) are coordinates, e is energy, and d is a derived feature- Euclidean distance between the ...
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26
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Feature Engineering a Recency feature
I have a customer scoring problem I'm working on specifically on predicting conversion and coming up with a probability score on conversion (using xgboost classifier atm). There's a feature I want to ...
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3
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283
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Using training data that requires manual interpretation
I have a dataset that comprises several data streams that are measured on objects (>10k objects). The data is essentially time series data (0.5 second intervals). Typically, an expert interpreter ...
- 155
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10
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Feature Selection in no labeled data
I'm new to this field and trying to learn by working with a fraud dataset. Initially, I used the dataset as is, but now I'm trying unsupervised learning without the labels. I've tried clustering ...
- 101
4
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2
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211
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What is the best way to train a neural network with a variable number of inputs?
Suppose I have a neural network with 5 inputs: [A,B,C,D,E]
There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are ...
- 41
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1
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26
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Dealing with only categorical features dataset
I'm trying to do multi-class classification on a labeled dataset with purely categorical features. There are around 30 features in total. 3 of the features in particular have around 100 unique values (...
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28
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Can my LSTM model learn feature engineering on its own?
I have a timeseries dataset and I am training an LSTM model on it to perform multiclass classification.
My dataset has 7 columns => x1,x2,x3....x7
And has 4 labels => f1,f2,f3,f4
Since I have ...
-1
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1
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159
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Can Machine Learning Algorithms Process Contextual Features for Regression?
Take Figure 1 showing point interpolation, where point L0 is being interpolated using points L2 and L1 and the distances L11, L12, L21, and L22.
Whilst the graph shows a linear interpolation example, ...
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126
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Improving the performance of gradient boosting classifier
I am training a gradient boosting classifier on an imbalanced data but the model is not performing very well. These are the things I have done to improve the model's performance.
Balanced the data ...
1
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1
answer
58
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How to normalize the features without the knowledge of the min and max values in online learning?
I am developing an online learning platform where input features are gathered from various sensors. However, these features may have vastly different ranges. For example, displacement values may be ...
- 15
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51
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How do I use ML models to estimate current stress level based on past data?
I am new to machine learning and I cannot understand the difference between estimating current stress level and predicting future stress levels based on historical data. I have been told these are two ...
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25
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Preprocessing overheads in Machine Learning
Meta reports that data preprocessing overheads is fast becoming a bottleneck to machine learning training (https://engineering.fb.com/2022/09/19/ml-applications/data-ingestion-machine-learning-...
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20
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Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes?
Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes? Please see the following.
- 101
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27
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Incorporate a new feature or Post-process
Briefly, I am training a model using XGBoost to predict future quantity for the factory to produce. Basic features currently in use are date time features, categories, holiday (binary). I have just ...
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