Questions tagged [ensemble-modeling]
In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.
196 questions
3 votes
1 answer
53 views
Boosting for vector regression a discussion
I am working on a deep learning problem. The output of the model is a 3D geometric vector that will represent the physical quantity “force”. I have a very heavy architecture that takes really long to ...
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3 votes
3 answers
268 views
Target imbalance observed but still model predicts correctly
I am currently working on the dataset where I am supposed to work on the prediction of the rides that might be cancelled. If it is predicted that it will be cancelled(because of drivers), then the ...
- 187
1 vote
1 answer
79 views
Manual Python Implementation of Stacking Model
I tried to build a Python class, CustomStackingClassifier(), to implement the Stacking method in ensemble machine learning. In this implementation, the output of the base classifiers is set to be the ...
- 13
0 votes
0 answers
26 views
3 votes
1 answer
305 views
What ML model for regression given tabular AND image data?
I'd like to predict the power production of a windfarm given the wind speed, its direction and other variables related to the specific wind turbines. However, due to wake effects (wind speed decreases ...
- 133
1 vote
0 answers
80 views
Uncertainty in stacked ensemble model
I am using the stacked generalization scheme to combine the predictions from different machine learning models (input models from now on). I am currently calculating the prediction interval for each ...
- 111
0 votes
0 answers
55 views
Select estimators for Voting classifier
I don't know the efficient method to select the best subset of the estimator that saves time and highest accuracy. How to choose the best estimators for the Voting classifier? Voting Classifier is an ...
0 votes
0 answers
34 views
How to use two independent datasets in machine learning phd research work?
In order to develop an academic performance prediction model for a local Higher Ed Institution, I have collected the OULAD open dataset and the local Institution's dataset which I structured into the ...
- 1
1 vote
2 answers
197 views
Is a Random Forest Capable of Learning and Predicting Numerical Trends in Panel Data?
In a panel data set consisting of exponential functions, each indexed by an integer i ranging from 0 to 100. The exponential function is defined as f(i, t) = A(i) * e^(-r(i) * t), where A(i) is the ...
- 301
0 votes
0 answers
141 views
Need Expert Advice for using sklearn pipeline to create a composite estimator with multiple models & features. Column Transformer with Mixed Types
I am trying to use sklearn pipeline to create a composite estimator. Please check attached image for model blueprint. Can anyone help me understand how this can be done in python. Column Transformer ...
0 votes
1 answer
44 views
how to build model using two input dataset in which there is no common column to merge or combine
I want to create model for truck company in which trucks delivers the car for customers.i have two data sets. one is customer details like how many cars they want from particular area or terminal and ...
- 1
0 votes
1 answer
91 views
Adding new model to ensemble without fitting again
Currently i have the following code ...
1 vote
1 answer
34 views
Regression models that conform to functional groupings of features
For example, suppose we want to predict y with features x1, x2, x3, x4. If I specify ...
- 11
0 votes
1 answer
2k views
How to reduce the false positives to improve the models performance?
I am currently building a binary classification model to predict order return rates. I used the GradientBoostingClassifier for training the model and also performed hyperparameter tuning using ...
0 votes
0 answers
64 views
Need help with improving validation loss and model overfitting/underfitting
I am using Ensemble PyTorch to train a voting classifier. My dataset includes around 60k records. I trained a Neural Network with Cross-entropy loss. Below is my model architecture ...
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