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1500 questions
10
votes
2 answers
Machine Learning Steps
Which of the below set of steps options is the correct one when creating a predictive model?
Option 1:
First eliminate the most obviously bad predictors, and preprocess the remaining if needed, then train various models with cross-validation, pick…
A K
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- 4
10
votes
4 answers
What initial steps should I use to make sense of large data sets, and what tools should I use?
Caveat: I am a complete beginner when it comes to machine learning, but eager to learn.
I have a large dataset and I'm trying to find pattern in it. There may / may not be correlation across the data, either with known variables, or variables that…
user3791372
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- 14
9
votes
4 answers
How to combine PCA and MCA on mixed data?
Suppose I have mixed data and (python) code which is capable of doing PCA (principal component analysis) on continuous predictors and MCA (multiple correspondence analysis) on nominal predictors. Is it possible to combine results from PCA and MCA…
Wojciech J. Migda
- 191
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9
votes
3 answers
What is the difference between one-hot and dummy encoding?
I am trying to understand
The reason behind encoding (one-hot encoding and dummy encoding)
How one-hot and dummy are different from each other
user121028
9
votes
1 answer
What tokenizer does OpenAI's GPT3 API use?
I'm building an application for the API, but I would like to be able to count the number of tokens my prompt will use, before I submit an API call. Currently I often submit prompts that yield a 'too-many-tokens' error.
The closest I got to an answer…
Herman Autore
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9
votes
1 answer
what is the difference between "fully developed decision trees" and "shallow decision trees"?
As reading Ensemble methods on scikit-learn docs, it says that
bagging methods work best with strong and complex models (e.g., fully
developed decision trees), in contrast with boosting methods which
usually work best with weak models (e.g.,…
Mithril
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9
votes
3 answers
Export weights (formula) from Random Forest Regressor in Scikit-Learn
I trained a prediction model with Scikit Learn in Python (Random Forest Regressor) and I want to extract somehow the weights of each feature to create an excel tool for manual prediction.
The only thing that I found is the model.feature_importances_…
Tasos
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9
votes
2 answers
Ethical consequences of non-deterministic learning processes?
Most advanced supervised learning techniques are non-deterministic by construction. The final output of the model usually depends on some random parts of the learning process. (Random weight initialization for Neural Networks or variable selection /…
Lucas Morin
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9
votes
1 answer
Where does the name 'LSTM' come from?
Long short-term memory is a recurrent neural network architecture introduced in the paper Long short-term memory.
Can you please tell me where the name comes from?
("Memory", as the network can store information because of the recurrence - but where…
Martin Thoma
- 18,880
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9
votes
1 answer
Properties for building a Multilayer Perceptron Neural Network using Keras?
I am trying to build and train a multilayer perceptron neural network that correctly predicts what president won in what county for the first time. I have the following information for training data.
Total population Median age % BachelorsDeg or…
pr338
- 385
- 2
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9
votes
1 answer
How to customise cost function in Scikit learn's model？
For example, when I have a problem that false negative should be penalised more, how can I incorporate that requirement in the algorithm such as SVM?
Ghostintheshell
- 431
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9
votes
1 answer
What is the difference between affinity matrix eigenvectors and graph Laplacian eigenvectors in the context of spectral clustering?
In spectral clustering, it's standard practice to solve the eigenvector problem
$$L v = \lambda v$$
where $L$ is the graph Laplacian, $v$ is the eigenvector related to eigenvalue $\lambda$.
My question: why bother taking the graph Laplacian?…
felipeduque
- 201
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9
votes
7 answers
Python library that can compute the confusion matrix for multi-label classification
I'm looking for a Python library that can compute the confusion matrix for multi-label classification.
FYI:
scikit-learn doesn't support multi-label for confusion matrix)
What is the difference between Multiclass and Multilabel Problem
Franck Dernoncourt
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9
votes
6 answers
Which cross-validation type best suits to binary classification problem
Data set looks like:
25000 observations
up to 15 predictors of different types: numeric, multi-class categorical, binary
target variable is binary
Which cross validation method is typical for this type of problems?
By default I'm using K-Fold. How…
IgorS
- 5,474
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9
votes
1 answer
What are the inputs to the first decoder layer in a Transformer model during the training phase?
I am trying to wrap my head around how the Transformer architecture works. I think I have a decent top-level understanding of the encoder part, sort of how the Key, Query, and Value tensors work in the MultiHead attention layers. What I am…
djvaroli
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