neal
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Pickled machine learning models
2 votes

For deep learning, there are a few model hubs where folks share models that are suitable for further fine-tuning or usage in given areas. None of these will be in the pickle format (from your question)...

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Effect of removing duplicates on Random Forest Regression
1 votes

I agree with the previous answer about your specific question: your model is likely to be less accurate if you just remove training data. Moreover: With very large sample counts some models (like ...

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Classification with feature not available at time of model creation
Accepted answer
1 votes

There are several different ways to formulate the "probability of solving a task" problem, as either a classification or regression. Each formulation would require you to transform your data ...

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Extract key phrases for binary outcome
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1 votes

There are a variety of techniques that you could use, depending on what you would like to do. If your goal is to gain insight into the phrases that are being used in each group, then I'd recommend ...

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Would a neural network trained on extracted features have the same accuracy as a full network with frozen layers?
Accepted answer
0 votes

When layers are "frozen" it generally means that their weights are not updated when backpropagation happens. So, technically, there is no difference between: Using a "frozen" ...

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upload model to S3
0 votes

To expand on the other answer: this is a problem that I've run into several times myself, and so I've built an open source modelstore library that automates this step - as well as doing other things ...

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Model Dump Parser (like XGBFI) for LightGBM and CatBoost
0 votes

Both LightGBM and CatBoost have functions that give you the models' feature importances. They are described in the respective docs here: LightGBM: plot_importance and lightgbm.importance Catboost: ...

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Loading models from external source
0 votes

I had also run into this problem several times, so I've created an open source modelstore Python library which seeks to tackle the problem of simplifying the best practices around versioning, storing, ...

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How to train a model to predict if 2 samples refer to the same thing?
0 votes

If you want to take a supervised approach, you can treat this as a binary classification problem where the input is two rows that have been concatenated into one, and the target output is the label ...

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Looking for binary class datasets with high class imbalance, that also have intra-class imbalance in the minority class
Accepted answer
0 votes

The imblearn.datasets package (documentation is here) has a function called fetch_datasets() which is described as: fetch_datasets allows to fetch 27 datasets which are imbalanced and binarized I do ...

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Best practices to store Python machine learning models
0 votes

I had also run into this problem several times, so I've created an open source modelstore Python library which seeks to tackle the problem of simplifying the best practices around versioning, storing, ...

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