Python vs R for machine learning - Data Science Stack Exchange
Jun 12, 2014 · I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people …
Best practices to store Python machine learning models
What are the best practices to save, store, and share machine learning models? In Python, we generally store the binary representation of the model, using pickle or joblib.
python - What do x and y mean when working with data in the …
Dec 14, 2021 · What do x and y mean when working with data in the machine learning domain? Ask Question Asked 3 years, 11 months ago Modified 2 years, 6 months ago
python - How do I handle string feature while performing model ...
Feb 15, 2019 · Polynomial Coding Sum Coding Target Encoder Weight of Evidence Wrappers Quantile Encoder Summary Encoder A Kaggle notebook - 11 Categorical Encoders and …
python - Which machine learning model is best for a combination …
Oct 11, 2021 · Which machine learning model is best for a combination of numerical and categorical data? Ask Question Asked 4 years, 1 month ago Modified 4 years, 1 month ago
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machine learning - How to train ML model with multiple variables ...
Oct 2, 2018 · machine-learning python scikit-learn Improve this question edited Oct 2, 2018 at 0:41 The Lyrist
machine learning - How can I check the correlation between …
Now using some machine learning on this data is not likely to work. There just is not sufficient data to extract some relevant information between your large number of features and the loan …
python - Incremental Learning with sklearn: warm_start, partial_fit ...
Answering my own question after some investigation: warm_start=True and calling .fit() sequentially should not be used for incremental learning on new datasets with potential …
python - What's the difference between fit and fit_transform in …
So for training set, you need to both calculate and do transformation. But for testing set, machine learning applies prediction based on what was learned during the training set and so it doesn't …